{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Serafeim Loukas, PhD",
  "title_listed": "How To Perform Feature Selection for Regression Problems",
  "keywords": "",
  "domain": "机器学习",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/how-to-perform-feature-selection-for-regression-problems-c928e527bbfa/",
  "publish_date": "2020-08-08",
  "corpus_stage": "",
  "notes": "",
  "id": "1edf34ee0e73",
  "collected_at": "2026-10-02T08:14:00",
  "title": "How To Perform Feature Selection for Regression Problems | Towards Data Science",
  "body_markdown": "![Revisit consent button](../images/body-df29beacd7496220cc94.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](../images/body-2159bb67f3de8ad870eb.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n  * Cookie\nBCTempID\n  * Duration\n10 minutes\n  * Description\nNo description available.\n\n\n  * Cookie\n__cf_bm\n  * Duration\n1 hour\n  * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n  * Cookie\nAWSALBCORS\n  * Duration\n7 days\n  * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n  * Cookie\n_cfuvid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n  * Cookie\nli_gc\n  * Duration\n6 months\n  * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n  * Cookie\n__hssrc\n  * Duration\nsession\n  * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n  * Cookie\n__hssc\n  * Duration\n1 hour\n  * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n  * Cookie\nwpEmojiSettingsSupports\n  * Duration\nsession\n  * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n  * Cookie\nBCSessionID\n  * Duration\n1 year 1 month 4 days\n  * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n  * Cookie\n_octo\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nlogged_in\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\n__Secure-YEC\n  * Duration\npast\n  * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n  * Cookie\n__eoi\n  * Duration\n6 months\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTGCORS\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\nlogin-status-p\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTG\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\ncsrf_token\n  * Duration\nsession\n  * Description\nNo description available.\n\n\n  * Cookie\ntoken_v2\n  * Duration\n1 day\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nD\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nPHPSESSID\n  * Duration\nsession\n  * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n  * Cookie\nVISITOR_PRIVACY_METADATA\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n  * Cookie\ncookietest\n  * Duration\nsession\n  * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n  * Cookie\n__Host-airtable-session\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\n__Host-airtable-session.sig\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\nm\n  * Duration\n1 year 1 month 4 days\n  * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n  * Cookie\nBIGipServer*\n  * Duration\nsession\n  * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n  * Cookie\n__cfruid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n  * Cookie\n_GRECAPTCHA\n  * Duration\n6 months\n  * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n  * Cookie\n__Secure-YNID\n  * Duration\n6 months\n  * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n  * Cookie\ncookieyes-consent\n  * Duration\n1 year\n  * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n  * Cookie\nlidc\n  * Duration\n1 day\n  * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n  * Cookie\nbrw\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nbrwConsent\n  * Duration\n5 minutes\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nWMF-Uniq\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nloom_anon_comment\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nloom_referral_video\n  * Duration\nsession\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nVISITOR_INFO1_LIVE\n  * Duration\n6 months\n  * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n  * Cookie\nyt-remote-connected-devices\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n  * Duration\nNever Expires\n  * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n  * Cookie\nyt-remote-device-id\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-name\n  * Duration\nsession\n  * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\nyt-remote-fast-check-period\n  * Duration\nsession\n  * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-app\n  * Duration\nsession\n  * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-available\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-installed\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\ncp_session\n  * Duration\n3 months\n  * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n  * Cookie\nloid\n  * Duration\n1 year 1 month 4 days\n  * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n  * Cookie\n__hstc\n  * Duration\n6 months\n  * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n  * Cookie\nhubspotutk\n  * Duration\n6 months\n  * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n  * Cookie\n_ga\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n  * Cookie\n_ga_*\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n  * Cookie\n__Host-psifi.analyticsTrace\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n__Host-psifi.analyticsTraceV2\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n_gh_sess\n  * Duration\nsession\n  * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n  * Cookie\nYSC\n  * Duration\nsession\n  * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n  * Cookie\najs_anonymous_id\n  * Duration\n1 year\n  * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n  * Cookie\nvuid\n  * Duration\n1 year 1 month 4 days\n  * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n  * Cookie\nAWSALB\n  * Duration\n7 days\n  * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n  * Cookie\nacq\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nacq.sig\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nptc\n  * Duration\n2 years\n  * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n  * Cookie\nmuc_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n  * Cookie\nguest_id_marketing\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\nguest_id_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\npersonalization_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n  * Cookie\nguest_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n  * Cookie\nyt.innertube::nextId\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nyt.innertube::requests\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nsession_tracker\n  * Duration\nsession\n  * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# How To Perform Feature Selection for Regression Problems\nIn this article I explain what feature selection is and how to perform it before training a regression model in Python.\n[Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\nAugust 8, 20206 min read\n![](../images/000.png)\n## 1. Introduction\n### What is **feature selection** ?\n> **Feature selection** is the procedure of selecting a **subset** (some out of all available) of the **input** **variables** that are most relevant to the target variable (that we wish to predict).\n**Target variable** here **** refers to the** variabl**e that we wish to** predic**t.\nFor this article we will assume that we only have numerical input variables and a numerical target for regression predictive modeling. Assuming that, we can easily estimate the **relationship** between each **input** variable and the **target** variable. This relationship can be established by calculating a metric such as the correlation value for example.\n* * *\n  * **NEW** : After a great deal of hard work and staying behind the scenes for quite a while, we're excited to now offer our expertise through a platform, the \"[**Data Science Hub**](https://www.patreon.com/TheDataScienceHub\\))\" on Patreon (<https://www.patreon.com/TheDataScienceHub>). This hub is our way of providing you with **bespoke consulting services** and comprehensive **responses to all your inquiries** , ranging from Machine Learning to strategic data analytics planning.\n\n\n* * *\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## 2. The main numerical feature selection methods\nThe 2 most famous feature selection techniques that can be used for numerical input data and a numerical target variable are the following:\n  * Correlation (Pearson, spearman)\n  * Mutual Information (MI, normalized MI)\n\n\n**Correlation** is a measure of how two variables change together. The most widely used correlation measure is the Pearson's correlation that assumes a Gaussian distribution of each variable and detects linear relationship between numerical variables.\nThis is done in 2 steps:\n  1. The **correlation** between each regressor and the target is computed, that is, ((X[:, i] - mean(X[:, i])) _(y - mean_y)) / (std(X[:, i])_ std(y)).\n  2. It is converted to an **F score** then to a **p-value**.\n\n\n* * *\n**Mutual information** originates from the field of information theory. The idea is that the information gain (typically used in the construction of decision trees) is applied in order to perform the feature selection. Mutual information is calculated between two variables and measures as the reduction in uncertainty for one variable given a known value of the other variable.\n* * *\n## 3. The dataset\nWe will use the **boston** **house** -**prices** **dataset**. This dataset contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. The dataset consists of the following variables:\n  1. CRIM - per capita crime rate by town\n  2. ZN - proportion of residential land zoned for lots over 25,000 sq.ft.\n  3. INDUS - proportion of non-retail business acres per town.\n  4. CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise)\n  5. NOX - nitric oxides concentration (parts per 10 million)\n  6. RM - average number of rooms per dwelling\n  7. AGE - proportion of owner-occupied units built prior to 1940\n  8. DIS - weighted distances to five Boston employment centres\n  9. RAD - index of accessibility to radial highways\n  10. TAX - full-value property-tax rate per $10,000\n  11. PTRATIO - pupil-teacher ratio by town\n  12. B - 1000(Bk - 0.63)² where Bk is the proportion of blacks by town\n  13. LSTAT - % lower status of the population\n  14. MEDV - Median value of owner-occupied homes in $1000's\n\n\n* * *\n## 4. Python Code & Working Example\nLet's load and split the dataset into training (70%) and test (30%) sets.\ntext\n\n```\nfrom sklearn.datasets import load_boston\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.feature_selection import f_regression\nimport matplotlib.pyplot as plt\nfrom sklearn.feature_selection import mutual_info_regression\n```\n\ntext\n\n```\n# load the data\nX, y = load_boston(return_X_y=True)\n```\n\ntext\n\n```\n# split into train and test sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0)\n```\n\nWe will use the well known scikit-learn machine library.\n### Case 1: Feature selection using the Correlation metric\nFor the **correlation statistic** we will use the [f_regression() function](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html). This function can be used in a feature selection strategy, such as selecting the top k most relevant features (largest values) via the [SelectKBest class](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html).\ntext\n\n```\n# feature selection\nf_selector = SelectKBest(score_func=f_regression, k='all')\n```\n\ntext\n\n```\n# learn relationship from training data\nf_selector.fit(X_train, y_train)\n```\n\ntext\n\n```\n# transform train input data\nX_train_fs = f_selector.transform(X_train)\n```\n\ntext\n\n```\n# transform test input data\nX_test_fs = f_selector.transform(X_test)\n```\n\ntext\n\n```\n# Plot the scores for the features\nplt.bar([i for i in range(len(f_selector.scores_))], f_selector.scores_)\n```\n\ntext\n\n```\nplt.xlabel(\"feature index\")\nplt.ylabel(\"F-value (transformed from the correlation values)\")\nplt.show()\n```\n\n**Reminder** : For the correlation statistic case:\n  1. The correlation between each regressor and the target is computed, that is, ((X[:, i] - mean(X[:, i])) _(y - mean_y)) / (std(X[:, i])_ std(y)).\n  2. It is converted to an **F score** then to a **p-value**.\n\n\n![Feature Importance plot](../images/001.png)\nFeature Importance plot\n**The plot above shows that feature 6 and 13 are more important than the other features. The y-axis represents the F-values that were estimated from the correlation values.**\n* * *\n### Case 2: Feature selection using the Mutual Information metric\nThe scikit-learn machine learning library provides an implementation of **mutual information** for feature selection with numeric input and output variables via the [mutual_info_regression() function](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_regression.html).\ntext\n\n```\n# feature selection\nf_selector = SelectKBest(score_func=mutual_info_regression, k='all')\n```\n\ntext\n\n```\n# learn relationship from training data\nf_selector.fit(X_train, y_train)\n```\n\ntext\n\n```\n# transform train input data\nX_train_fs = f_selector.transform(X_train)\n```\n\ntext\n\n```\n# transform test input data\nX_test_fs = f_selector.transform(X_test)\n```\n\ntext\n\n```\n# Plot the scores for the features\nplt.bar([i for i in range(len(f_selector.scores_))], f_selector.scores_)\n```\n\ntext\n\n```\nplt.xlabel(\"feature index\")\nplt.ylabel(\"Estimated MI value\")\nplt.show()\n```\n\n![Feature Importance plot](../images/002.png)\nFeature Importance plot\n**The y-axis represents the estimated mutual information between each feature and the target variable. Compared to the correlation feature selection method we can clearly see many more features scored as being relevant. This may be because of the statistical noise that might exists in the dataset.**\n* * *\n## 5. Conclusion\nIn this article I have provided two ways in order to perform feature selection. **Feature selection** is the procedure of selecting a subset (some out of all available) of the input variables that are most relevant to the target variable. **Target variable** here **** refers to the** variabl**e that we wish to** predic**t.\nUsing either the **Correlation** metric or the **Mutual** **Information** metric , we can easily estimate the **relationship** between each **input** variable and the **target** variable.\n**Correlation** **vs** **Mutual** **Information:** Compared to the correlation feature selection method we can clearly see many more features scored as being relevant. This may be because of the statistical noise that might exists in the dataset.\n* * *\n### You might also like:\n> [**LSTM Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model**](https://towardsdatascience.com/lstm-time-series-forecasting-predicting-stock-prices-using-an-lstm-model-6223e9644a2f)\n> [**Time-Series Forecasting: Predicting Stock Prices Using An ARIMA Model**](https://towardsdatascience.com/time-series-forecasting-predicting-stock-prices-using-an-arima-model-2e3b3080bd70)\n* * *\n## Stay tuned & support this effort\nIf you liked and found this article useful, **follow** me to be able to see all my new posts.\nQuestions? Post them as a comment and I will reply as soon as possible.\n* * *\n## Latest posts\n> [**The Best FREE Data Science Resources: FREE Books & Online Courses**](https://medium.com/@seralouk/the-best-free-data-science-resources-free-books-online-courses-9c4a2df194e5)\n> [**ROC Curve Explained using a COVID-19 hypothetical example: Binary & Multi-Class Classification...**](https://towardsdatascience.com/roc-curve-explained-using-a-covid-19-hypothetical-example-binary-multi-class-classification-bab188ea869c)\n> [**Support Vector Machines (SVM) clearly explained: A python tutorial for classification problems...**](https://towardsdatascience.com/support-vector-machines-svm-clearly-explained-a-python-tutorial-for-classification-problems-29c539f3ad8)\n> [**PCA clearly explained - How, when, why to use it and feature importance: A guide in Python**](https://towardsdatascience.com/pca-clearly-explained-how-when-why-to-use-it-and-feature-importance-a-guide-in-python-7c274582c37e)\n> [**Everything you need to know about Min-Max normalization in Python**](https://towardsdatascience.com/everything-you-need-to-know-about-min-max-normalization-in-python-b79592732b79)\n> [**How Scikit-Learn's StandardScaler works**](https://towardsdatascience.com/how-and-why-to-standardize-your-data-996926c2c832)\n* * *\n## Get in touch with me\n  * **LinkedIn** : <https://www.linkedin.com/in/serafeim-loukas/>\n  * **ResearchGate** : <https://www.researchgate.net/profile/Serafeim_Loukas>\n  * **EPFL** **profile** : <https://people.epfl.ch/serafeim.loukas>\n  * **Stack** **Overflow** : <https://stackoverflow.com/users/5025009/seralouk>\n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](../images/body-e55101a9bb3902e3fc4b.png)\nWritten By\n## [Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\n[See all from Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\n[Data Science](https://towardsdatascience.com/tag/data-science/)[Feature Selection](https://towardsdatascience.com/tag/feature-selection/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Python](https://towardsdatascience.com/tag/python/)[Regression](https://towardsdatascience.com/tag/regression/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-perform-feature-selection-for-regression-problems-c928e527bbfa%2F&title=How%20To%20Perform%20Feature%20Selection%20for%20Regression%20Problems)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-perform-feature-selection-for-regression-problems-c928e527bbfa%2F&title=How%20To%20Perform%20Feature%20Selection%20for%20Regression%20Problems)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-perform-feature-selection-for-regression-problems-c928e527bbfa%2F&text=How%20To%20Perform%20Feature%20Selection%20for%20Regression%20Problems)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n  * [![](../images/003.jpeg)](https://towardsdatascience.com/llmops-production-prompt-engineering-patterns-with-hamilton-5c3a20178ad2/)\n## [LLMOps: Production prompt engineering patterns with Hamilton](https://towardsdatascience.com/llmops-production-prompt-engineering-patterns-with-hamilton-5c3a20178ad2/)\n[](https://towardsdatascience.com/how-to-perform-feature-selection-for-regression-problems-c928e527bbfa/)\nAn overview of the production grade ways to iterate on prompts with Hamilton.\n[Stefan Krawczyk](https://towardsdatascience.com/author/stefan.krawczyk/)\nSeptember 13, 202314 min read\n  * [![](../images/004.jpg)](https://towardsdatascience.com/graph-rag-automated-prompt-engineering-agent-frameworks-and-other-september-must-reads-18ae79f105a7/)\n## [Graph RAG, Automated Prompt Engineering, Agent Frameworks, and Other September Must-Reads](https://towardsdatascience.com/graph-rag-automated-prompt-engineering-agent-frameworks-and-other-september-must-reads-18ae79f105a7/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nThe stories that resonated the most with our community in the past month\n[TDS Editors](https://towardsdatascience.com/author/towardsdatascience/)\nOctober 3, 20243 min read\n  * [![](../images/005.png)](https://towardsdatascience.com/teaching-cars-to-see-vehicle-detection-using-machine-learning-and-computer-vision-54628888079a/)\n## [Teaching Cars To See - Vehicle Detection Using Machine Learning And Computer Vision](https://towardsdatascience.com/teaching-cars-to-see-vehicle-detection-using-machine-learning-and-computer-vision-54628888079a/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nThis is the final project of Term 1 of the Udacity Self-Driving Car Engineer Nanodegree. You can find all code related to this project on...\n[Eddie Forson](https://towardsdatascience.com/author/ed_forson/)\nOctober 25, 201711 min read\n  * [![](../images/006.png)](https://towardsdatascience.com/from-econometrics-to-machine-learning-ee182f3a45d7/)\n## [From Econometrics to Machine Learning](https://towardsdatascience.com/from-econometrics-to-machine-learning-ee182f3a45d7/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nWhy econometrics should be part of your skills\n[Alexandre Warembourg](https://towardsdatascience.com/author/alexandrewarembourg/)\nSeptember 8, 20197 min read\n  * [![](../images/body-63beb50f664a055ed739.jpg)](https://towardsdatascience.com/latest-picks-8-common-pitfalls-of-running-a-b-tests-66a15a22913b/)\n## [Latest picks: 8 Common Pitfalls of Running A/B Tests](https://towardsdatascience.com/latest-picks-8-common-pitfalls-of-running-a-b-tests-66a15a22913b/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nYour daily dose of data science\n[TDS Editors](https://towardsdatascience.com/author/towardsdatascience/)\nFebruary 24, 20211 min read\n  * [![](../images/008.jpeg)](https://towardsdatascience.com/3-things-you-need-to-know-before-working-with-images-in-machine-learning-6a2ab6f6b822/)\n## [3 Things You Need To Know Before Working With Images In Machine Learning](https://towardsdatascience.com/3-things-you-need-to-know-before-working-with-images-in-machine-learning-6a2ab6f6b822/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nMaster the basics before taking on computer vision projects\n[Aashish Nair](https://towardsdatascience.com/author/aashishnair/)\nJanuary 10, 20225 min read\n  * [![](../images/009.png)](https://towardsdatascience.com/how-is-linear-algebra-applied-for-machine-learning-d193bdeed268/)\n## [Linear Algebra for ML | Matrix, Vector and Data Representation](https://towardsdatascience.com/how-is-linear-algebra-applied-for-machine-learning-d193bdeed268/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nStarting from using matrix and vector for data representation\n[Destin Gong](https://towardsdatascience.com/author/destingong/)\nDecember 30, 20228 min read\n  * [![](../images/010.png)](https://towardsdatascience.com/understand-polars-lack-of-indexes-526ea75e413/)\n## [Understand Polars' Lack of Indexes](https://towardsdatascience.com/understand-polars-lack-of-indexes-526ea75e413/)\n[](https://towardsdatascience.com/how-to-perform-feature-selection-for-regression-problems-c928e527bbfa/)\nSwitch from Pandas to Polars and forget about indexes\n[Carl Kadie](https://towardsdatascience.com/author/carlmkadie/)\nJanuary 6, 20236 min read\n  * [![](../images/011.png)](https://towardsdatascience.com/visualizing-audio-pipelines-with-streamlit-96525781b5d9/)\n## [Visualizing Audio Pipelines with Streamlit](https://towardsdatascience.com/visualizing-audio-pipelines-with-streamlit-96525781b5d9/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nExamine augmentations right in your browser\n[Pascal Janetzky](https://towardsdatascience.com/author/pascaljanetzky/)\nJune 13, 20213 min read\n\n\n  * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n  * [X](https://x.com/TDataScience)\n  * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n  * [Threads](https://www.threads.net/@towardsdatascience)\n  * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n####  Recommended Articles \nClose\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500) How Relevance Models Foreshadowed Transformers for NLP ](https://towardsdatascience.com/how-relevance-models-foreshadowed-transformers-for-nlp/ \"How Relevance Models Foreshadowed Transformers for NLP\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n\n\n",
  "metadata": {
    "title": "How To Perform Feature Selection for Regression Problems | Towards Data Science",
    "description": "In this article I explain what feature selection is and how to perform it before training a regression model in Python.",
    "keywords": null,
    "author": "Serafeim Loukas, PhD",
    "og:locale": "en_US",
    "og:type": "article",
    "og:title": "How To Perform Feature Selection for Regression Problems",
    "og:description": "In this article I explain what feature selection is and how to perform it before training a regression model in Python.",
    "og:url": "https://towardsdatascience.com/how-to-perform-feature-selection-for-regression-problems-c928e527bbfa/",
    "og:site_name": "Towards Data Science",
    "og:image": "https://assets.insightmediagroup.io/media/wp-content/uploads/2020/08/1XgGa0BE3mTXBHdzGHHp62w.png",
    "og:image:width": "1820",
    "og:image:height": "1228",
    "og:image:type": "image/png",
    "twitter:card": "summary_large_image",
    "twitter:site": "@TDataScience",
    "twitter:creator": "@TDataScience",
    "twitter:label1": "Written by",
    "twitter:data1": "Serafeim Loukas, PhD",
    "twitter:label2": "Est. reading time",
    "twitter:data2": "6 min read",
    "article:published_time": "2020-08-08T12:44:44.000Z",
    "article:modified_time": "2020-08-08T12:44:44.000Z",
    "article:author": "https://towardsdatascience.com/author/seralouk/"
  },
  "paywall": {
    "listed_paid": false,
    "detected": false,
    "marker": "",
    "public_only": false,
    "policy": "付费/会员内容只保留页面公开可见部分，不绕过付费墙"
  },
  "crawl": {
    "success": true,
    "status_code": 202,
    "error": ""
  },
  "code_blocks": [
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_000.txt",
      "chars": 288
    },
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_001.txt",
      "chars": 52
    },
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_002.txt",
      "chars": 123
    },
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_003.txt",
      "chars": 79
    },
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_004.txt",
      "chars": 73
    },
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_005.txt",
      "chars": 72
    },
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_006.txt",
      "chars": 69
    },
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_007.txt",
      "chars": 108
    },
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_008.txt",
      "chars": 103
    },
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_009.txt",
      "chars": 89
    },
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_010.txt",
      "chars": 73
    },
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_011.txt",
      "chars": 72
    },
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_012.txt",
      "chars": 69
    },
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_013.txt",
      "chars": 108
    },
    {
      "language": "txt",
      "file": "attachments/1edf34ee0e73/code/block_014.txt",
      "chars": 72
    }
  ],
  "images": [
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T18:06:17Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T18:06:17Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T18:06:17Z"
    },
    {
      "status": "http_404",
      "url": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500",
      "src": "https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500",
      "alt": "",
      "downloaded": false,
      "discovery": "saved_markdown",
      "retried_at": "2026-10-20T18:06:17Z"
    }
  ],
  "audio": [],
  "videos": [
    {
      "url": "https://www.youtube.com/c/TowardsDataScience",
      "kind": "channel_reference",
      "status": "reference_only"
    }
  ],
  "links": [
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/latest/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/editors-pick/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/the-variable/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/shipai/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/submissions/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/category/data-science/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/author/seralouk/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/lstm-time-series-forecasting-predicting-stock-prices-using-an-lstm-model-6223e9644a2f"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/time-series-forecasting-predicting-stock-prices-using-an-arima-model-2e3b3080bd70"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/roc-curve-explained-using-a-covid-19-hypothetical-example-binary-multi-class-classification-bab188ea869c"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/support-vector-machines-svm-clearly-explained-a-python-tutorial-for-classification-problems-29c539f3ad8"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/pca-clearly-explained-how-when-why-to-use-it-and-feature-importance-a-guide-in-python-7c274582c37e"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/everything-you-need-to-know-about-min-max-normalization-in-python-b79592732b79"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/how-and-why-to-standardize-your-data-996926c2c832"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/data-science/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/feature-selection/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/machine-learning/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/python/"
    },
    {
      "type": "internal",
      "href": "https://towardsdatascience.com/tag/regression/"
    },
    {
      "type": "external",
      "href": "https://write.insightmediagroup.io"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/company/towards-data-science/"
    },
    {
      "type": "external",
      "href": "https://x.com/TDataScience"
    },
    {
      "type": "external",
      "href": "https://www.patreon.com/TheDataScienceHub)"
    },
    {
      "type": "external",
      "href": "https://www.patreon.com/TheDataScienceHub"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh/ai-data-scientist"
    },
    {
      "type": "external",
      "href": "https://roadmap.sh/machine-learning"
    },
    {
      "type": "external",
      "href": "https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html"
    },
    {
      "type": "external",
      "href": "https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html"
    },
    {
      "type": "external",
      "href": "https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_regression.html"
    },
    {
      "type": "external",
      "href": "https://medium.com/@seralouk/the-best-free-data-science-resources-free-books-online-courses-9c4a2df194e5"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/in/serafeim-loukas/"
    },
    {
      "type": "external",
      "href": "https://www.researchgate.net/profile/Serafeim_Loukas"
    },
    {
      "type": "external",
      "href": "https://people.epfl.ch/serafeim.loukas"
    },
    {
      "type": "external",
      "href": "https://stackoverflow.com/users/5025009/seralouk"
    },
    {
      "type": "external",
      "href": "https://www.facebook.com/sharer/sharer.php?title=How+To+Perform+Feature+Selection+for+Regression+Problems&u=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-perform-feature-selection-for-regression-problems-c928e527bbfa%2F"
    },
    {
      "type": "external",
      "href": "https://www.linkedin.com/shareArticle?mini=true&title=How+To+Perform+Feature+Selection+for+Regression+Problems&url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-perform-feature-selection-for-regression-problems-c928e527bbfa%2F"
    },
    {
      "type": "external",
      "href": "https://x.com/share?text=How+To+Perform+Feature+Selection+for+Regression+Problems&url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-perform-feature-selection-for-regression-problems-c928e527bbfa%2F"
    },
    {
      "type": "external",
      "href": "https://www.youtube.com/c/TowardsDataScience"
    },
    {
      "type": "external",
      "href": "https://www.threads.net/@towardsdatascience"
    }
  ],
  "repair_version": 2,
  "repaired_at": "2026-10-20T17:32:36Z",
  "source_url": "https://towardsdatascience.com/how-to-perform-feature-selection-for-regression-problems-c928e527bbfa/",
  "delivery_status": "unverified",
  "discovery_status": "http_403",
  "missing_reason": "源页面未重新核验:http_403；已按保留正文补采图片",
  "repair_error": "",
  "actual_video_files": [],
  "expected_saved_body_images": 4,
  "image_discovery": "saved_markdown",
  "source_body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. By clicking \"Accept All\", you consent to our use of cookies.\nCustomise Reject All Accept All\nCustomise Consent Preferences ![](https://cdn-cookieyes.com/assets/images/close.svg)\nWe use cookies to help you navigate efficiently and perform certain functions. You will find detailed information about all cookies under each consent category below.\nThe cookies that are categorised as \"Necessary\" are stored on your browser as they are essential for enabling the basic functionalities of the site. ... Show more\nNecessaryAlways Active\nNecessary cookies are required to enable the basic features of this site, such as providing secure log-in or adjusting your consent preferences. These cookies do not store any personally identifiable data.\n  * Cookie\nBCTempID\n  * Duration\n10 minutes\n  * Description\nNo description available.\n\n\n  * Cookie\n__cf_bm\n  * Duration\n1 hour\n  * Description\nThis cookie, set by Cloudflare, is used to support Cloudflare Bot Management. \n\n\n  * Cookie\nAWSALBCORS\n  * Duration\n7 days\n  * Description\nAmazon Web Services set this cookie for load balancing.\n\n\n  * Cookie\n_cfuvid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to track users across sessions to optimize user experience by maintaining session consistency and providing personalized services\n\n\n  * Cookie\nli_gc\n  * Duration\n6 months\n  * Description\nLinkedin set this cookie for storing visitor's consent regarding using cookies for non-essential purposes.\n\n\n  * Cookie\n__hssrc\n  * Duration\nsession\n  * Description\nThis cookie is set by Hubspot whenever it changes the session cookie. The __hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session.\n\n\n  * Cookie\n__hssc\n  * Duration\n1 hour\n  * Description\nHubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the __hstc cookie.\n\n\n  * Cookie\nwpEmojiSettingsSupports\n  * Duration\nsession\n  * Description\nWordPress sets this cookie when a user interacts with emojis on a WordPress site. It helps determine if the user's browser can display emojis properly.\n\n\n  * Cookie\nBCSessionID\n  * Duration\n1 year 1 month 4 days\n  * Description\nBlueconic sets this cookie as a unique identifier for the BlueConic profile.\n\n\n  * Cookie\n_octo\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nlogged_in\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\n__Secure-YEC\n  * Duration\npast\n  * Description\nYouTube sets this cookie to stores the user's video player preferences using embedded YouTube video\n\n\n  * Cookie\n__eoi\n  * Duration\n6 months\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTGCORS\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\nlogin-status-p\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nAWSALBTG\n  * Duration\n7 days\n  * Description\nNo description available.\n\n\n  * Cookie\ncsrf_token\n  * Duration\nsession\n  * Description\nNo description available.\n\n\n  * Cookie\ntoken_v2\n  * Duration\n1 day\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nD\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nPHPSESSID\n  * Duration\nsession\n  * Description\nThis cookie is native to PHP applications. The cookie stores and identifies a user's unique session ID to manage user sessions on the website. The cookie is a session cookie and will be deleted when all the browser windows are closed.\n\n\n  * Cookie\nVISITOR_PRIVACY_METADATA\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to store the user's cookie consent state for the current domain. \n\n\n  * Cookie\ncookietest\n  * Duration\nsession\n  * Description\nThe cookietest cookie is typically used to determine whether the user's browser accepts cookies, essential for website functionality and user experience.\n\n\n  * Cookie\n__Host-airtable-session\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\n__Host-airtable-session.sig\n  * Duration\n1 year\n  * Description\nThis cookie is used to enable us to integrate the services of Airtable.\n\n\n  * Cookie\nm\n  * Duration\n1 year 1 month 4 days\n  * Description\nStripe sets this cookie for fraud prevention purposes. It identifies the device used to access the website, allowing the website to be formatted accordingly.\n\n\n  * Cookie\nBIGipServer*\n  * Duration\nsession\n  * Description\nMarketo sets this cookie to collect information about the user's online activity and build a profile about their interests to provide advertisements relevant to the user. \n\n\n  * Cookie\n__cfruid\n  * Duration\nsession\n  * Description\nCloudflare sets this cookie to identify trusted web traffic.\n\n\n  * Cookie\n_GRECAPTCHA\n  * Duration\n6 months\n  * Description\nGoogle Recaptcha service sets this cookie to identify bots to protect the website against malicious spam attacks.\n\n\n  * Cookie\n__Secure-YNID\n  * Duration\n6 months\n  * Description\nGoogle cookie used to protect user security and prevent fraud, especially during the login process.\n\n\n  * Cookie\ncookieyes-consent\n  * Duration\n1 year\n  * Description\nCookieYes sets this cookie to remember users' consent preferences so that their preferences are respected on subsequent visits to this site. It does not collect or store any personal information about the site visitors.\n\n\nFunctional\nFunctional cookies help perform certain functionalities like sharing the content of the website on social media platforms, collecting feedback, and other third-party features.\n  * Cookie\nlidc\n  * Duration\n1 day\n  * Description\nLinkedIn sets the lidc cookie to facilitate data center selection.\n\n\n  * Cookie\nbrw\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nbrwConsent\n  * Duration\n5 minutes\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nWMF-Uniq\n  * Duration\n1 year\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nloom_anon_comment\n  * Duration\n1 year\n  * Description\nNo description available.\n\n\n  * Cookie\nloom_referral_video\n  * Duration\nsession\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nVISITOR_INFO1_LIVE\n  * Duration\n6 months\n  * Description\nA cookie set by YouTube to measure bandwidth that determines whether the user gets the new or old player interface.\n\n\n  * Cookie\nyt-remote-connected-devices\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nytidb::LAST_RESULT_ENTRY_KEY\n  * Duration\nNever Expires\n  * Description\nThe cookie ytidb::LAST_RESULT_ENTRY_KEY is used by YouTube to store the last search result entry that was clicked by the user. This information is used to improve the user experience by providing more relevant search results in the future.\n\n\n  * Cookie\nyt-remote-device-id\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to store the user's video preferences using embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-name\n  * Duration\nsession\n  * Description\nThe yt-remote-session-name cookie is used by YouTube to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\nyt-remote-fast-check-period\n  * Duration\nsession\n  * Description\nThe yt-remote-fast-check-period cookie is used by YouTube to store the user's video player preferences for embedded YouTube videos.\n\n\n  * Cookie\nyt-remote-session-app\n  * Duration\nsession\n  * Description\nThe yt-remote-session-app cookie is used by YouTube to store user preferences and information about the interface of the embedded YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-available\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-available cookie is used to store the user's preferences regarding whether casting is available on their YouTube video player.\n\n\n  * Cookie\nyt-remote-cast-installed\n  * Duration\nsession\n  * Description\nThe yt-remote-cast-installed cookie is used to store the user's video player preferences using embedded YouTube video.\n\n\n  * Cookie\ncp_session\n  * Duration\n3 months\n  * Description\nCodepen sets this cookie for Help systems found in the website.\n\n\n  * Cookie\nloid\n  * Duration\n1 year 1 month 4 days\n  * Description\nThis cookie is set by the Reddit. The cookie enables the sharing of content from the website onto the social media platform.\n\n\nAnalytics\nAnalytical cookies are used to understand how visitors interact with the website. These cookies help provide information on metrics such as the number of visitors, bounce rate, traffic source, etc.\n  * Cookie\n__hstc\n  * Duration\n6 months\n  * Description\nHubspot set this main cookie for tracking visitors. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session).\n\n\n  * Cookie\nhubspotutk\n  * Duration\n6 months\n  * Description\nHubSpot sets this cookie to keep track of the visitors to the website. This cookie is passed to HubSpot on form submission and used when deduplicating contacts.\n\n\n  * Cookie\n_ga\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to calculate visitor, session and campaign data and track site usage for the site's analytics report. The cookie stores information anonymously and assigns a randomly generated number to recognise unique visitors.\n\n\n  * Cookie\n_ga_*\n  * Duration\n1 year 1 month 4 days\n  * Description\nGoogle Analytics sets this cookie to store and count page views.\n\n\n  * Cookie\n__Host-psifi.analyticsTrace\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n__Host-psifi.analyticsTraceV2\n  * Duration\n6 hours\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\n_gh_sess\n  * Duration\nsession\n  * Description\nGitHub sets this cookie for temporary application and framework state between pages like what step the user is on in a multiple step form.\n\n\n  * Cookie\nYSC\n  * Duration\nsession\n  * Description\nYSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages.\n\n\n  * Cookie\najs_anonymous_id\n  * Duration\n1 year\n  * Description\nThis cookie is set by Segment to count the number of people who visit a certain site by tracking if they have visited before.\n\n\n  * Cookie\nvuid\n  * Duration\n1 year 1 month 4 days\n  * Description\nVimeo installs this cookie to collect tracking information by setting a unique ID to embed videos on the website. \n\n\nPerformance\nPerformance cookies are used to understand and analyse the key performance indexes of the website which helps in delivering a better user experience for the visitors.\n  * Cookie\nAWSALB\n  * Duration\n7 days\n  * Description\nAWSALB is an application load balancer cookie set by Amazon Web Services to map the session to the target.\n\n\n  * Cookie\nacq\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nacq.sig\n  * Duration\npast\n  * Description\nDescription is currently not available.\n\n\n  * Cookie\nptc\n  * Duration\n2 years\n  * Description\nNo description available.\n\n\nAdvertisement\nAdvertisement cookies are used to provide visitors with customised advertisements based on the pages you visited previously and to analyse the effectiveness of the ad campaigns.\n  * Cookie\nmuc_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to collect user behaviour and interaction data to optimize the website.\n\n\n  * Cookie\nguest_id_marketing\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\nguest_id_ads\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor.\n\n\n  * Cookie\npersonalization_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to integrate and share features for social media and also store information about how the user uses the website, for tracking and targeting.\n\n\n  * Cookie\nguest_id\n  * Duration\n1 year 1 month 4 days\n  * Description\nTwitter sets this cookie to identify and track the website visitor. It registers if a user is signed in to the Twitter platform and collects information about ad preferences.\n\n\n  * Cookie\nbcookie\n  * Duration\n1 year\n  * Description\nLinkedIn sets this cookie from LinkedIn share buttons and ad tags to recognize browser IDs.\n\n\n  * Cookie\n__Secure-ROLLOUT_TOKEN\n  * Duration\n6 months\n  * Description\nYouTube sets this cookie to manage feature rollout and experimentation. It helps Google control which new features or interface changes are shown to users as part of testing and staged rollouts, ensuring consistent experience for a given user during an experiment.\n\n\n  * Cookie\nyt.innertube::nextId\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nyt.innertube::requests\n  * Duration\nNever Expires\n  * Description\nYouTube sets this cookie to register a unique ID to store data on what videos from YouTube the user has seen.\n\n\n  * Cookie\nsession_tracker\n  * Duration\nsession\n  * Description\nThis cookie is set by the Reddit. This cookie is used to identify trusted web traffic. It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# How To Perform Feature Selection for Regression Problems\nIn this article I explain what feature selection is and how to perform it before training a regression model in Python.\n[Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\nAugust 8, 20206 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/08/1XgGa0BE3mTXBHdzGHHp62w.png)\n## 1. Introduction\n### What is **feature selection** ?\n> **Feature selection** is the procedure of selecting a **subset** (some out of all available) of the **input** **variables** that are most relevant to the target variable (that we wish to predict).\n**Target variable** here **** refers to the** variabl**e that we wish to** predic**t.\nFor this article we will assume that we only have numerical input variables and a numerical target for regression predictive modeling. Assuming that, we can easily estimate the **relationship** between each **input** variable and the **target** variable. This relationship can be established by calculating a metric such as the correlation value for example.\n* * *\n  * **NEW** : After a great deal of hard work and staying behind the scenes for quite a while, we're excited to now offer our expertise through a platform, the \"[**Data Science Hub**](https://www.patreon.com/TheDataScienceHub\\))\" on Patreon (<https://www.patreon.com/TheDataScienceHub>). This hub is our way of providing you with **bespoke consulting services** and comprehensive **responses to all your inquiries** , ranging from Machine Learning to strategic data analytics planning.\n\n\n* * *\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## 2. The main numerical feature selection methods\nThe 2 most famous feature selection techniques that can be used for numerical input data and a numerical target variable are the following:\n  * Correlation (Pearson, spearman)\n  * Mutual Information (MI, normalized MI)\n\n\n**Correlation** is a measure of how two variables change together. The most widely used correlation measure is the Pearson's correlation that assumes a Gaussian distribution of each variable and detects linear relationship between numerical variables.\nThis is done in 2 steps:\n  1. The **correlation** between each regressor and the target is computed, that is, ((X[:, i] - mean(X[:, i])) _(y - mean_y)) / (std(X[:, i])_ std(y)).\n  2. It is converted to an **F score** then to a **p-value**.\n\n\n* * *\n**Mutual information** originates from the field of information theory. The idea is that the information gain (typically used in the construction of decision trees) is applied in order to perform the feature selection. Mutual information is calculated between two variables and measures as the reduction in uncertainty for one variable given a known value of the other variable.\n* * *\n## 3. The dataset\nWe will use the **boston** **house** -**prices** **dataset**. This dataset contains information collected by the U.S Census Service concerning housing in the area of Boston Mass. The dataset consists of the following variables:\n  1. CRIM - per capita crime rate by town\n  2. ZN - proportion of residential land zoned for lots over 25,000 sq.ft.\n  3. INDUS - proportion of non-retail business acres per town.\n  4. CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise)\n  5. NOX - nitric oxides concentration (parts per 10 million)\n  6. RM - average number of rooms per dwelling\n  7. AGE - proportion of owner-occupied units built prior to 1940\n  8. DIS - weighted distances to five Boston employment centres\n  9. RAD - index of accessibility to radial highways\n  10. TAX - full-value property-tax rate per $10,000\n  11. PTRATIO - pupil-teacher ratio by town\n  12. B - 1000(Bk - 0.63)² where Bk is the proportion of blacks by town\n  13. LSTAT - % lower status of the population\n  14. MEDV - Median value of owner-occupied homes in $1000's\n\n\n* * *\n## 4. Python Code & Working Example\nLet's load and split the dataset into training (70%) and test (30%) sets.\ntext\n\n```\nfrom sklearn.datasets import load_boston\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_selection import SelectKBest\nfrom sklearn.feature_selection import f_regression\nimport matplotlib.pyplot as plt\nfrom sklearn.feature_selection import mutual_info_regression\n```\n\ntext\n\n```\n# load the data\nX, y = load_boston(return_X_y=True)\n```\n\ntext\n\n```\n# split into train and test sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30, random_state=0)\n```\n\nWe will use the well known scikit-learn machine library.\n### Case 1: Feature selection using the Correlation metric\nFor the **correlation statistic** we will use the [f_regression() function](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html). This function can be used in a feature selection strategy, such as selecting the top k most relevant features (largest values) via the [SelectKBest class](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.SelectKBest.html).\ntext\n\n```\n# feature selection\nf_selector = SelectKBest(score_func=f_regression, k='all')\n```\n\ntext\n\n```\n# learn relationship from training data\nf_selector.fit(X_train, y_train)\n```\n\ntext\n\n```\n# transform train input data\nX_train_fs = f_selector.transform(X_train)\n```\n\ntext\n\n```\n# transform test input data\nX_test_fs = f_selector.transform(X_test)\n```\n\ntext\n\n```\n# Plot the scores for the features\nplt.bar([i for i in range(len(f_selector.scores_))], f_selector.scores_)\n```\n\ntext\n\n```\nplt.xlabel(\"feature index\")\nplt.ylabel(\"F-value (transformed from the correlation values)\")\nplt.show()\n```\n\n**Reminder** : For the correlation statistic case:\n  1. The correlation between each regressor and the target is computed, that is, ((X[:, i] - mean(X[:, i])) _(y - mean_y)) / (std(X[:, i])_ std(y)).\n  2. It is converted to an **F score** then to a **p-value**.\n\n\n![Feature Importance plot](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/08/1Tp_-XnDug0ZNIBA9ywOm9w.png)\nFeature Importance plot\n**The plot above shows that feature 6 and 13 are more important than the other features. The y-axis represents the F-values that were estimated from the correlation values.**\n* * *\n### Case 2: Feature selection using the Mutual Information metric\nThe scikit-learn machine learning library provides an implementation of **mutual information** for feature selection with numeric input and output variables via the [mutual_info_regression() function](https://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.mutual_info_regression.html).\ntext\n\n```\n# feature selection\nf_selector = SelectKBest(score_func=mutual_info_regression, k='all')\n```\n\ntext\n\n```\n# learn relationship from training data\nf_selector.fit(X_train, y_train)\n```\n\ntext\n\n```\n# transform train input data\nX_train_fs = f_selector.transform(X_train)\n```\n\ntext\n\n```\n# transform test input data\nX_test_fs = f_selector.transform(X_test)\n```\n\ntext\n\n```\n# Plot the scores for the features\nplt.bar([i for i in range(len(f_selector.scores_))], f_selector.scores_)\n```\n\ntext\n\n```\nplt.xlabel(\"feature index\")\nplt.ylabel(\"Estimated MI value\")\nplt.show()\n```\n\n![Feature Importance plot](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/08/1d1L_O9q0TGS55EjPVvgEDA.png)\nFeature Importance plot\n**The y-axis represents the estimated mutual information between each feature and the target variable. Compared to the correlation feature selection method we can clearly see many more features scored as being relevant. This may be because of the statistical noise that might exists in the dataset.**\n* * *\n## 5. Conclusion\nIn this article I have provided two ways in order to perform feature selection. **Feature selection** is the procedure of selecting a subset (some out of all available) of the input variables that are most relevant to the target variable. **Target variable** here **** refers to the** variabl**e that we wish to** predic**t.\nUsing either the **Correlation** metric or the **Mutual** **Information** metric , we can easily estimate the **relationship** between each **input** variable and the **target** variable.\n**Correlation** **vs** **Mutual** **Information:** Compared to the correlation feature selection method we can clearly see many more features scored as being relevant. This may be because of the statistical noise that might exists in the dataset.\n* * *\n### You might also like:\n> [**LSTM Time-Series Forecasting: Predicting Stock Prices Using An LSTM Model**](https://towardsdatascience.com/lstm-time-series-forecasting-predicting-stock-prices-using-an-lstm-model-6223e9644a2f)\n> [**Time-Series Forecasting: Predicting Stock Prices Using An ARIMA Model**](https://towardsdatascience.com/time-series-forecasting-predicting-stock-prices-using-an-arima-model-2e3b3080bd70)\n* * *\n## Stay tuned & support this effort\nIf you liked and found this article useful, **follow** me to be able to see all my new posts.\nQuestions? Post them as a comment and I will reply as soon as possible.\n* * *\n## Latest posts\n> [**The Best FREE Data Science Resources: FREE Books & Online Courses**](https://medium.com/@seralouk/the-best-free-data-science-resources-free-books-online-courses-9c4a2df194e5)\n> [**ROC Curve Explained using a COVID-19 hypothetical example: Binary & Multi-Class Classification...**](https://towardsdatascience.com/roc-curve-explained-using-a-covid-19-hypothetical-example-binary-multi-class-classification-bab188ea869c)\n> [**Support Vector Machines (SVM) clearly explained: A python tutorial for classification problems...**](https://towardsdatascience.com/support-vector-machines-svm-clearly-explained-a-python-tutorial-for-classification-problems-29c539f3ad8)\n> [**PCA clearly explained - How, when, why to use it and feature importance: A guide in Python**](https://towardsdatascience.com/pca-clearly-explained-how-when-why-to-use-it-and-feature-importance-a-guide-in-python-7c274582c37e)\n> [**Everything you need to know about Min-Max normalization in Python**](https://towardsdatascience.com/everything-you-need-to-know-about-min-max-normalization-in-python-b79592732b79)\n> [**How Scikit-Learn's StandardScaler works**](https://towardsdatascience.com/how-and-why-to-standardize-your-data-996926c2c832)\n* * *\n## Get in touch with me\n  * **LinkedIn** : <https://www.linkedin.com/in/serafeim-loukas/>\n  * **ResearchGate** : <https://www.researchgate.net/profile/Serafeim_Loukas>\n  * **EPFL** **profile** : <https://people.epfl.ch/serafeim.loukas>\n  * **Stack** **Overflow** : <https://stackoverflow.com/users/5025009/seralouk>\n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657496994_4frvt5.png)\nWritten By\n## [Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\n[See all from Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\n[Data Science](https://towardsdatascience.com/tag/data-science/)[Feature Selection](https://towardsdatascience.com/tag/feature-selection/)[Machine Learning](https://towardsdatascience.com/tag/machine-learning/)[Python](https://towardsdatascience.com/tag/python/)[Regression](https://towardsdatascience.com/tag/regression/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-perform-feature-selection-for-regression-problems-c928e527bbfa%2F&title=How%20To%20Perform%20Feature%20Selection%20for%20Regression%20Problems)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-perform-feature-selection-for-regression-problems-c928e527bbfa%2F&title=How%20To%20Perform%20Feature%20Selection%20for%20Regression%20Problems)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-perform-feature-selection-for-regression-problems-c928e527bbfa%2F&text=How%20To%20Perform%20Feature%20Selection%20for%20Regression%20Problems)\n\n\nTowards Data Science is a community publication. Submit your insights to reach our global audience and earn through the TDS Author Payment Program.\n[Write for TDS](https://towardsdatascience.com/submissions/)\n## Related Articles\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/09/1e3dMFCcQ8wp5kZxgRWoGoA.jpeg)](https://towardsdatascience.com/llmops-production-prompt-engineering-patterns-with-hamilton-5c3a20178ad2/)\n## [LLMOps: Production prompt engineering patterns with Hamilton](https://towardsdatascience.com/llmops-production-prompt-engineering-patterns-with-hamilton-5c3a20178ad2/)\n[](https://towardsdatascience.com/how-to-perform-feature-selection-for-regression-problems-c928e527bbfa/)\nAn overview of the production grade ways to iterate on prompts with Hamilton.\n[Stefan Krawczyk](https://towardsdatascience.com/author/stefan.krawczyk/)\nSeptember 13, 202314 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2024/10/09-QfLq-UsC0xIYea-scaled.jpg)](https://towardsdatascience.com/graph-rag-automated-prompt-engineering-agent-frameworks-and-other-september-must-reads-18ae79f105a7/)\n## [Graph RAG, Automated Prompt Engineering, Agent Frameworks, and Other September Must-Reads](https://towardsdatascience.com/graph-rag-automated-prompt-engineering-agent-frameworks-and-other-september-must-reads-18ae79f105a7/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nThe stories that resonated the most with our community in the past month\n[TDS Editors](https://towardsdatascience.com/author/towardsdatascience/)\nOctober 3, 20243 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2017/10/1Ef6QU6hyuyvvqVfl87a1qw.png)](https://towardsdatascience.com/teaching-cars-to-see-vehicle-detection-using-machine-learning-and-computer-vision-54628888079a/)\n## [Teaching Cars To See - Vehicle Detection Using Machine Learning And Computer Vision](https://towardsdatascience.com/teaching-cars-to-see-vehicle-detection-using-machine-learning-and-computer-vision-54628888079a/)\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\nThis is the final project of Term 1 of the Udacity Self-Driving Car Engineer Nanodegree. You can find all code related to this project on...\n[Eddie Forson](https://towardsdatascience.com/author/ed_forson/)\nOctober 25, 201711 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2019/09/1yUTYScHKNMtkyXbbNjx1Yw.png)](https://towardsdatascience.com/from-econometrics-to-machine-learning-ee182f3a45d7/)\n## [From Econometrics to Machine Learning](https://towardsdatascience.com/from-econometrics-to-machine-learning-ee182f3a45d7/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nWhy econometrics should be part of your skills\n[Alexandre Warembourg](https://towardsdatascience.com/author/alexandrewarembourg/)\nSeptember 8, 20197 min read\n  * [![](https://towardsdatascience.com/images/fallback-image.jpg)](https://towardsdatascience.com/latest-picks-8-common-pitfalls-of-running-a-b-tests-66a15a22913b/)\n## [Latest picks: 8 Common Pitfalls of Running A/B Tests](https://towardsdatascience.com/latest-picks-8-common-pitfalls-of-running-a-b-tests-66a15a22913b/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nYour daily dose of data science\n[TDS Editors](https://towardsdatascience.com/author/towardsdatascience/)\nFebruary 24, 20211 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/01/1xx7LP6f9flaa212cvsiTzw-scaled.jpeg)](https://towardsdatascience.com/3-things-you-need-to-know-before-working-with-images-in-machine-learning-6a2ab6f6b822/)\n## [3 Things You Need To Know Before Working With Images In Machine Learning](https://towardsdatascience.com/3-things-you-need-to-know-before-working-with-images-in-machine-learning-6a2ab6f6b822/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nMaster the basics before taking on computer vision projects\n[Aashish Nair](https://towardsdatascience.com/author/aashishnair/)\nJanuary 10, 20225 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/12/1TrKE4T_q795SlHZlsuT4Zw.png)](https://towardsdatascience.com/how-is-linear-algebra-applied-for-machine-learning-d193bdeed268/)\n## [Linear Algebra for ML | Matrix, Vector and Data Representation](https://towardsdatascience.com/how-is-linear-algebra-applied-for-machine-learning-d193bdeed268/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nStarting from using matrix and vector for data representation\n[Destin Gong](https://towardsdatascience.com/author/destingong/)\nDecember 30, 20228 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/01/1FtaveEZLaXcqMXg0RfYrqQ.png)](https://towardsdatascience.com/understand-polars-lack-of-indexes-526ea75e413/)\n## [Understand Polars' Lack of Indexes](https://towardsdatascience.com/understand-polars-lack-of-indexes-526ea75e413/)\n[](https://towardsdatascience.com/how-to-perform-feature-selection-for-regression-problems-c928e527bbfa/)\nSwitch from Pandas to Polars and forget about indexes\n[Carl Kadie](https://towardsdatascience.com/author/carlmkadie/)\nJanuary 6, 20236 min read\n  * [![](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/06/15FvGbJG-k_4-peiuhfcXOQ.png)](https://towardsdatascience.com/visualizing-audio-pipelines-with-streamlit-96525781b5d9/)\n## [Visualizing Audio Pipelines with Streamlit](https://towardsdatascience.com/visualizing-audio-pipelines-with-streamlit-96525781b5d9/)\n[Machine Learning](https://towardsdatascience.com/category/artificial-intelligence/machine-learning/)\nExamine augmentations right in your browser\n[Pascal Janetzky](https://towardsdatascience.com/author/pascaljanetzky/)\nJune 13, 20213 min read\n\n\n  * [YouTube](https://www.youtube.com/c/TowardsDataScience)\n  * [X](https://x.com/TDataScience)\n  * [LinkedIn](https://www.linkedin.com/company/towards-data-science/)\n  * [Threads](https://www.threads.net/@towardsdatascience)\n  * [Bluesky](https://bsky.app/profile/towardsdatascience.com)\n\n\n[](https://towardsdatascience.com/)\nYour home for data science and AI. The world's leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.\nSubscribe to Our Newsletter[Write For TDS](https://towardsdatascience.com/submissions/)•[About](https://towardsdatascience.com/about-towards-data-science-d691af11cc2f/)•[Advertise](https://contact.towardsdatascience.com/advertise-with-towards-data-science)•[Privacy Policy](https://towardsdatascience.com/privacy-policy/)•[Terms of Use](https://towardsdatascience.com/website-terms-of-use/)\n© Insight Media Group, LLC 2026•[The New Stack](https://thenewstack.io)•[roadmap.sh](https://roadmap.sh)•[Creator Network](https://insightmediagroup.io/creator-network)\n####  Recommended Articles \nClose\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607686/image?etag=1782161464448&width=500&height=500) How Relevance Models Foreshadowed Transformers for NLP ](https://towardsdatascience.com/how-relevance-models-foreshadowed-transformers-for-nlp/ \"How Relevance Models Foreshadowed Transformers for NLP\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607639/image?etag=1781250125848&width=500&height=500) Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1 ](https://towardsdatascience.com/javascript-fatigue-you-dont-need-js-to-build-chatgpt/ \"Javascript Fatigue: HTMX Is All You Need to Build ChatGPT — Part 1\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/607930/image?etag=1782124566054&width=500&height=500) The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs ](https://towardsdatascience.com/subset-sum-problem-solved-in-linear-time-for-dense-enough-inputs/ \"The Subset Sum Problem Solved in Linear Time for Dense Enough Inputs\")\n  * [ ![](https://h030.towardsdatascience.com/rest/v2/contentStores/d7d3a4ab-7caf-4383-bbd2-fbf7fb4f1787/items/3660/image?etag=1780176246070&width=500&height=500) Does Your Company Have a Data Strategy? ](https://towardsdatascience.com/does-your-company-have-a-data-strategy-e8944dbec4ae/ \"Does Your Company Have a Data Strategy?\")\n\n\n",
  "body_base_path": "attachments/1edf34ee0e73/pages/",
  "body_markdown_path": "attachments/1edf34ee0e73/pages/archived-body.md",
  "local_html": "attachments/1edf34ee0e73/pages/archived-body.html",
  "body_source": "prior_capture"
}