{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Kurtis Pykes",
  "title_listed": "The Difference Between Classification and Regression in Machine Learning",
  "keywords": "分类,回归",
  "domain": "机器学习",
  "content_type": "深度技术解析",
  "is_paid_flag": "部分免费/会员墙",
  "url": "https://towardsdatascience.com/the-difference-between-classification-and-regression-in-machine-learning-4ccdb5b18fd3/",
  "publish_date": "2021-08",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "575bccdcc288",
  "collected_at": "2026-10-02T00:29:17",
  "title": "The Difference Between Classification and Regression in Machine Learning | Towards Data Science",
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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[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# The Difference Between Classification and Regression in Machine Learning\nThe Problem of Function Approximation\n[Kurtis Pykes](https://towardsdatascience.com/author/kurtispykes/)\nAugust 6, 20213 min read\n### The _**Problem of Function Approximation**_\n![Photo by Green Chameleon on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2021/08/0NTx-R2H3DVmeCfMy-scaled.jpg)\nPhoto by [Green Chameleon](https://unsplash.com/@craftedbygc?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\nDifferentiating between regression and classification algorithms can be challenging at the beginning of your machine learning career. When you've been around the field for coming up to 4 years, it's very easy to forget the challenges of learning machine learning. There's so much jargon thrown about which makes it difficult to find your bearings when you're just starting out.\nA vital part of machine learning is distinguishing whether a task is a regression or classification problem. This distinction provides practitioners with a clearer insight into what machine learning algorithms may be most suitable when approaching the problem since some models are more useful for classification than they are for regression - and vice versa.\nThe similarity between the 2 tasks is that they both are a form of _**supervised learning**_ _._\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [Computer Science](https://roadmap.sh/computer-science) and [Product Manager](https://roadmap.sh/product-manager) roadmaps.\n### Supervised Learning\nBoth regression and classification problems fall into the supervised learning category. Each task involves developing a model that learns from historical data which enables it to make predictions on new instances that we do not have answers for. More formally, all supervised learning tasks involve learning a function that maps an input to an output based on example input-output pairs.\nThe object of supervised learning problems is to approximate the mapping function (f) from the input features (X) to the output (y) - in Mathematics, this is known as the _**problem of function approximation**_.\nRegardless of whether we are attempting to solve a classification or regression problem, whenever we develop a learning algorithm for a supervised learning problem, the job of the algorithm is to find the best mapping function given the available resources.\n### The Difference - Classification vs Regression\nDespite the similarity in the overall goal (mapping inputs to outputs based on input-output mappings), classifiaction and regression problems are different.\nIn classification problems, our learning algorithm learns a function to map inputs to outputs where the output value is a discrete class label (i.e. cat or dog, malignant or benign, spam or non-spam, etc). Popular classification algorithms inlcude:\n  * [Logistic Regression](https://towardsdatascience.com/algorithms-from-scratch-logistic-regression-7bacdfd9738e?source=collection_tagged---------5----------------------------)\n  * [Naive Bayes](https://towardsdatascience.com/algorithms-from-scratch-naive-bayes-classifier-8006cc691493?source=collection_tagged---------1----------------------------)\n  * [K-Nearest Neighbors](https://towardsdatascience.com/algorithms-from-scratch-k-nearest-neighbors-fe19b431a57?source=collection_tagged---------2----------------------------)\n  * [Decision Tree](https://towardsdatascience.com/algorithms-from-scratch-decision-tree-1898d37b02e0?source=collection_tagged---------4----------------------------)\n  * [Support Vector Machines](https://towardsdatascience.com/algorithms-from-scratch-support-vector-machine-6f5eb72fce10?source=collection_tagged---------3----------------------------)\n\n\nIn contrast, regression problems are concerned with mapping inputs to outputs where the output is a continous real number (i.e. the price of a house). Popular regression algorithms include:\n  * [Linear Regression](https://towardsdatascience.com/algorithms-from-scratch-linear-regression-c654353d1e7c?source=collection_tagged---------6----------------------------)\n  * Ridge Regression\n  * Lasso Regression\n  * Neural Networks Regression\n  * [Decision Tree](https://neptune.ai/blog/fighting-overfitting-with-l1-or-l2-regularization)\n\n\n> **Note** : Learn more about Ridge and Lasso regression in [Fighting overfitting with L1 or L2 Regularization](https://neptune.ai/blog/fighting-overfitting-with-l1-or-l2-regularization)\nSome algorithms are either exclusively for regression style problems such as Linear Regression models, and some algorithms are exclusively for classification tasks such as Logistic regression. However, there are some algorithms that can overlap once small modifications are made to them such as Decision Trees, Random Forest, XGBoost, and Neural Networks.\n### Final Thoughts\nEssentially, the way we determine whether a task is a classification or regression problem is by the output. Regression tasks are concerned with predicting a continuous value, whereas classification tasks are concerned with predicting discrete values. Also, the way we evaluate each type of problem is different for example the mean squared error is a useful metric for regression tasks but this wouldn't work for a classification. Simarlarly, accuracy wouldn't be an effective metric for regression tasks but it can be useful for classification tasks.\nThanks for Reading!\nIf you enjoyed this article, connect with me by subscribing **** to my** FRE**E [weekly newsletter](https://mailchi.mp/ef1f7700a873/sign-up). Never miss a post I make about Artificial Intelligence, Data Science, and Freelancing.\n### Related Articles\n> [**The Difference Between Data Scientists and ML Engineers**](https://towardsdatascience.com/the-difference-between-data-scientists-and-ml-engineers-f6e797948003)\n> [**How To Become A Machine Learning Engineer**](https://towardsdatascience.com/how-to-become-a-machine-learning-engineer-e420e134c0a3)\n> [**The Day To Day Of A Machine Learning Engineer**](https://towardsdatascience.com/the-day-to-day-of-a-machine-learning-engineer-378b80bf1f6f)\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. 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