{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Serafeim Loukas, PhD",
  "title_listed": "Text Classification Using Naive Bayes: Theory & A Working Example",
  "keywords": "",
  "domain": "文本处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/text-classification-using-naive-bayes-theory-a-working-example-2ef4b7eb7d5a/",
  "publish_date": "2020-10-12",
  "corpus_stage": "",
  "notes": "",
  "id": "546e87609d3b",
  "collected_at": "2026-10-02T08:08:09",
  "title": "Text Classification Using Naive Bayes: Theory & A Working Example | 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[Probability](https://towardsdatascience.com/category/probability/)\n# Text Classification Using Naive Bayes: Theory & A Working Example\nIn this article, I explain how the Naive Bayes works and I implement a multi-class text classification problem step-by-step in Python.\n[Serafeim Loukas, PhD](https://towardsdatascience.com/author/seralouk/)\nOctober 12, 202010 min read\n### [Getting Started](https://towardsdatascience.com/tagged/getting-started)\n![Figure created by the author.](../images/000.png)\nFigure created by the author.\nLearn this step by step with the interactive [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n### Table of contents\n  1. **Introduction**\n  2. **The Naive Bayes algorithm**\n  3. **Dealing with text data**\n  4. **Working Example in Python (step-by-step guide)**\n  5. **Bonus: Having fun with the model**\n  6. **Conclusions**\n\n\n* * *\n## 1. Introduction\n**Naive Bayes** classifiers are a collection of classification algorithms based on **Bayes' Theorem**. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.\n**Naive Bayes** classifiers have been heavily used for **text classification** and **text** **analysis** machine learning **problems**.\n**Text Analysis** is a major application field for machine learning algorithms. However the raw data, a sequence of symbols (i.e. strings) cannot be fed directly to the algorithms themselves as most of them expect numerical feature vectors with a fixed size rather than the raw text documents with variable length.\nIn this article I explain a) how **Naive Bayes** works**,** b) how we can use **text** **data** and **fit** them into a **model** after transforming them into a more appropriate form. Finally, I **implement** a **multi-class text classification problem step-by-step in Python**.\nLet's get started !!!\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* * *\n## 2. The Naive Bayes algorithm\nNaive Bayes classifiers are a collection of classification algorithms based on **Bayes' Theorem**. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.\nThe dataset is divided into two parts, namely, **feature matrix** and the **response/target vector**.\n  * The **Feature** **matrix** (X) contains all the vectors(rows) of the dataset in which each vector consists of the value of **dependent features**. The number of features is **d** i.e. **X = (x1,x2,x2, xd).**\n  * The **Response/target** **vector** (y) contains the value of **class/group variable** for **each row of feature matrix.**\n\n\n### **2.1. The main two assumptions of Naive Bayes**\nNaive Bayes assumes that **each feature/variable** of the same class makes an:\n  * **independent**\n  * **equal**\n\n\n**contribution to the outcome.**\n**Side Note:** The assumptions made by Naive Bayes are not generally correct in real-world situations. In-fact, the independence assumption is often not met and this is why it is called \"**Naive** \" i.e. because it assumes something that might not be true.\n### 2.2. The Bayes' Theorem\nBayes' Theorem finds the probability of an event occurring given the probability of another event that has already occurred. Bayes' theorem is stated mathematically as follows:\n![Figure created by the author.](../images/001.png)\nFigure created by the author.\nwhere:\n  * **A** and **B** are called **events.**\n  * P(A | B) is the probability of event A, given the event B is true (has occured). Event B is also termed as **evidence**.\n  * P(A) is the **priori** of A (the prior independent probability, i.e. probability of event before evidence is seen).\n  * P(B | A) is the probability of B given event A, i.e. probability of event B after evidence A is seen.\n\n\n### Summary\n![Figure created by the author.](../images/002.png)\nFigure created by the author.\n### 2.3. The Naive Bayes Model\nGiven a data matrix **X** and a target vector **y,** we state our problem as:\n![Figure created by the author.](../images/003.png)\nFigure created by the author.\nwhere, **y** is **class variable** and **X** is a **dependent feature vector with dimension d i.e. X = (x1,x2,x2, xd),** where **d** is the number of variables/features of the sample.\n  * P(y|X) is the probability of observing the class **y** given the sample **X** with **X = (x1,x2,x2, xd),** where **d** is the number of variables/features of the sample.\n\n\nNow the \"naïve\" [conditional independence](https://en.wikipedia.org/wiki/Conditional_independence) assumptions come into play: assume that all features in **X** are [mutually independent](https://en.wikipedia.org/wiki/Mutually_independent), conditional on the category **y** :\n![Figure created by the author.](../images/004.png)\nFigure created by the author.\nFinally, to find the probability of a given **sample** for all possible values of the class variable _**y**_ , we just need to find the output with maximum probability:\n![Figure created by the author.](../images/005.png)\nFigure created by the author.\n* * *\n## 3. Dealing with text data\nOne question that arises at this point is the following:\n> How are we going to use the raw text data to train the model ? The raw data is a collection of strings !\nText Analysis is a major application field for machine learning algorithms. **However the raw data, a sequence of symbols (i.e. strings) cannot be fed directly to the algorithms themselves as most of them expect numerical feature vectors with a fixed size rather than the raw text documents with variable length.**\nIn order to address this, scikit-learn provides utilities for the most common ways to extract numerical features from text content, namely:\n  * **tokenizing** strings and giving an integer id for each possible token, for instance by using white-spaces and punctuation as token separators.\n  * **counting** the occurrences of tokens in each document.\n\n\nIn this scheme, features and samples are defined as follows:\n  * each **individual token occurrence frequency** is treated as a **feature**.\n  * the vector of all the token frequencies for a given **document** is considered a multivariate **sample**.\n\n\n### \"Counting\" Example (to really understand this before we move on):\ntext\n\n```\nfrom sklearn.feature_extraction.text import CountVectorizer\ncorpus = [\n    'This is the first document.',\n    'This document is the second document.',\n    'And this is the third one.',\n    'Is this the first document?',\n]\n```\n\ntext\n\n```\nvectorizer = CountVectorizer()\nX = vectorizer.fit_transform(corpus)\n```\n\ntext\n\n```\nprint(vectorizer.get_feature_names())\n['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']\n```\n\ntext\n\n```\nprint(X.toarray())\n[[0 1 1 1 0 0 1 0 1]\n [0 2 0 1 0 1 1 0 1]\n [1 0 0 1 1 0 1 1 1]\n [0 1 1 1 0 0 1 0 1]]\n```\n\nIn the above toy example, we have a collection of strings stored into the variable **corpus.** Using the **text** **transformer** , we can see that we have a specific number of unique strings (vocabulary) in our data.\nThis can be seen by printing the **vectorizer.get_feature_names()** variable**.** We observe that we have **9 unique words**.\nNext, we printed the transformed data (**X**) and **we observe the following** :\n  * We have 4 rows in X as the number of our text strings (**we have the same number of samples after the transformation**).\n  * **We have the same number of columns** (features/variables) in the transformed data (**X**) f**or all the samples (** this was not the case before that transformation i.e. the individual strings had different lengths).\n  * The values 0,1,2, encode the **frequency** of a **word** that **appeared** in the **initial text data.**\n\n\n**E.g**. The first transformed row is **[0 1 1 1 0 0 1 0 1]** and **** th** e unique vocabulary**i** s ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this'],** thus this means that the words \"document\", \"first\", \"is\", \"the\" and \"this\" appeared 1 time each in the initial text string (i.e. 'This is the first document.').\n**Side note:** This is the counting approach. The [term-frequency transform](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html#sklearn.feature_extraction.text.TfidfTransformer) is nothing more than a transformation of a count matrix into a normalized term-frequency matrix.\nHope everything is clear now. If not, read this paragraph as many times as it is needed in order to really grasp the idea and understand this transformation. **It is a fundamental step.**\n* * *\n## 4. Working example in Python\nNow that you understood how the Naive Bayes and the Text Transformation work, it's time to start coding !\n### Problem Statement\nAs a working example, we will use some **text** **data** and we will build a **Naive** **Bayes** model to **predict** the **categories** of the **texts**. This is a **multi-class (20 classes) text classification problem**.\nLet's start (I will walk you through). First, we will **load all the necessary libraries** :\ntext\n\n```\nimport numpy as np, pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.metrics import confusion_matrix, accuracy_score\n```\n\ntext\n\n```\nsns.set() # use seaborn plotting style\n```\n\nNext, **let's load the data** (**training** and **test** sets):\ntext\n\n```\n# Load the dataset\ndata = fetch_20newsgroups()\n```\n\ntext\n\n```\n# Get the text categories\ntext_categories = data.target_names\n```\n\ntext\n\n```\n# define the training set\ntrain_data = fetch_20newsgroups(subset=\"train\", categories=text_categories)\n```\n\ntext\n\n```\n# define the test set\ntest_data = fetch_20newsgroups(subset=\"test\", categories=text_categories)\n```\n\nLet's find out **how many classes** and **samples** we have:\ntext\n\n```\nprint(\"We have {} unique classes\".format(len(text_categories)))\nprint(\"We have {} training samples\".format(len(train_data.data)))\nprint(\"We have {} test samples\".format(len(test_data.data)))\n```\n\nThe above prints:\ntext\n\n```\nWe have 20 unique classes\nWe have 11314 training samples\nWe have 7532 test samples\n```\n\nSo, this is a **20-class text classification problem** with n_train = **11314** **training** **samples** (text sentences) and n_test = **7532** **test** **samples** (text sentences).\nLet's visualize the 5th training sample:\ntext\n\n```\n# let's have a look as some training data\nprint(test_data.data[5])\n```\n\nAs mentioned previously, our data are **texts** (more specifically, **emails**) so you should see something like the following printed out:\n![Figure created by the author.](../images/006.png)\nFigure created by the author.\nThe next step consists of building the **Naive Bayes classifier** and finally **training** the **model.** In our example, we will convert the collection of text documents (train and test sets) into a matrix of token counts (I explained how this works in **Section 3**).\n> To implement that text transformation we will use the **make_pipeline** function. This will internally transform the text data and then the model will be fitted **using the transformed data**.\ntext\n\n```\n# Build the model\nmodel = make_pipeline(TfidfVectorizer(), MultinomialNB())\n```\n\ntext\n\n```\n# Train the model using the training data\nmodel.fit(train_data.data, train_data.target)\n```\n\ntext\n\n```\n# Predict the categories of the test data\npredicted_categories = model.predict(test_data.data)\n```\n\nThe last line of code **predicts the labels of the test set**.\nLet's see the predicted categories names:\ntext\n\n```\nprint(np.array(test_data.target_names)[predicted_categories])\narray(['rec.autos', 'sci.crypt', 'alt.atheism', ..., 'rec.sport.baseball', 'comp.sys.ibm.pc.hardware', 'soc.religion.christian'], dtype='<U24')\n```\n\nFinally, let's build the **multi-class confusion matrix** to see if the model is good or if the model predicts correctly only specific text categories.\ntext\n\n```\n# plot the confusion matrix\nmat = confusion_matrix(test_data.target, predicted_categories)\nsns.heatmap(mat.T, square = True, annot=True, fmt = \"d\", xticklabels=train_data.target_names,yticklabels=train_data.target_names)\nplt.xlabel(\"true labels\")\nplt.ylabel(\"predicted label\")\nplt.show()\n```\n\ntext\n\n```\nprint(\"The accuracy is {}\".format(accuracy_score(test_data.target, predicted_categories)))\n```\n\ntext\n\n```\nThe accuracy is 0.7738980350504514\n```\n\n![Figure created by the author.](../images/007.png)\nFigure created by the author.\n* * *\n**Note:** If you're passionate about diving deeper into machine learning with Python and sklearn, I highly recommend checking out [**this book**](https://amzn.to/3t4YB2d); it's a game-changer in breaking down complex topics into digestible insights.\n> [**Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques...**](https://amzn.to/3t4YB2d)\n## 5. Bonus: Having fun with the model\nLet's have some fun using the trained model. Let's classify whatever sentence we like 😄 .\ntext\n\n```\n# custom function to have fun\ndef my_predictions(my_sentence, model):\n    all_categories_names = np.array(data.target_names)\n    prediction = model.predict([my_sentence])\n    return all_categories_names[prediction]\n```\n\ntext\n\n```\n\nmy_sentence = \"jesus\"\nprint(my_predictions(my_sentence, model))\n['soc.religion.christian']\n```\n\ntext\n\n```\nmy_sentence = \"Are you an atheist?\"\nprint(my_predictions(my_sentence, model))\n['alt.atheism']\n```\n\nWe inserted the string \"jesus\" to the model and it predicted the class \"['soc.religion.christian']\".\nChange the \"my_sentence\" into other stings and **play with the model** 😃 .\n* * *\n## 6. Conclusions\nWe saw that **Naive Bayes is a very powerful algorithm** for **multi-class text classification problems.**\n_**Side Note**_ : _If you want to know more about the confusion matrix (and the ROC curve) read this:_\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### Interpreting the confusion matrix\nFrom the above **confusion** **matrix** , we can verify that the model is really good.\n  * It is able to correctly predict all 20 classes of the text data (most values are on the diagonal and few are off-the-diagonal).\n  * We also notice that the highest miss-classification (value off-the-diagonal) is **131** (5 lines from the end, last column at the right). The value 131 means that 131 documents that belonged to the \"**religion miscellaneous** \" category were miss-classified as belonging to the \"**religion christian** \" category.\n\n\nInteresting thing that these 2 categories are really similar and actually one could characterize these as 2 subgroups of a larger group e.g. \"religion\" in general.\nFinally, the **accuracy** on the **test** set is **0.7739** which is quite good for a **20-class text classification problem** 🚀 .\n**That's all folks! Hope you liked this article.**\n* * *\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### My Profile (have a look to find out about my collection of articles):\n> [**Serafeim Loukas - Towards Data Science**](https://towardsdatascience.com/@seralouk)\n* * *\n## Get in touch with me\n  * **LinkedIn** : <https://www.linkedin.com/in/serafeim-loukas/>\n\n\n* * *\n### You may also like:\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> [**K-Means Clustering: How It Works & Finding The Optimum Number Of Clusters In The Data**](https://towardsdatascience.com/k-means-clustering-how-it-works-finding-the-optimum-number-of-clusters-in-the-data-13d18739255c)\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> [**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## 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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Introduction\n**Naive Bayes** classifiers are a collection of classification algorithms based on **Bayes' Theorem**. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.\n**Naive Bayes** classifiers have been heavily used for **text classification** and **text** **analysis** machine learning **problems**.\n**Text Analysis** is a major application field for machine learning algorithms. However the raw data, a sequence of symbols (i.e. strings) cannot be fed directly to the algorithms themselves as most of them expect numerical feature vectors with a fixed size rather than the raw text documents with variable length.\nIn this article I explain a) how **Naive Bayes** works**,** b) how we can use **text** **data** and **fit** them into a **model** after transforming them into a more appropriate form. Finally, I **implement** a **multi-class text classification problem step-by-step in Python**.\nLet's get started !!!\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* * *\n## 2. The Naive Bayes algorithm\nNaive Bayes classifiers are a collection of classification algorithms based on **Bayes' Theorem**. It is not a single algorithm but a family of algorithms where all of them share a common principle, i.e. every pair of features being classified is independent of each other.\nThe dataset is divided into two parts, namely, **feature matrix** and the **response/target vector**.\n  * The **Feature** **matrix** (X) contains all the vectors(rows) of the dataset in which each vector consists of the value of **dependent features**. The number of features is **d** i.e. **X = (x1,x2,x2, xd).**\n  * The **Response/target** **vector** (y) contains the value of **class/group variable** for **each row of feature matrix.**\n\n\n### **2.1. The main two assumptions of Naive Bayes**\nNaive Bayes assumes that **each feature/variable** of the same class makes an:\n  * **independent**\n  * **equal**\n\n\n**contribution to the outcome.**\n**Side Note:** The assumptions made by Naive Bayes are not generally correct in real-world situations. In-fact, the independence assumption is often not met and this is why it is called \"**Naive** \" i.e. because it assumes something that might not be true.\n### 2.2. The Bayes' Theorem\nBayes' Theorem finds the probability of an event occurring given the probability of another event that has already occurred. Bayes' theorem is stated mathematically as follows:\n![Figure created by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/10/1wtWUVjaqWORD4ViniVMN-Q.png)\nFigure created by the author.\nwhere:\n  * **A** and **B** are called **events.**\n  * P(A | B) is the probability of event A, given the event B is true (has occured). Event B is also termed as **evidence**.\n  * P(A) is the **priori** of A (the prior independent probability, i.e. probability of event before evidence is seen).\n  * P(B | A) is the probability of B given event A, i.e. probability of event B after evidence A is seen.\n\n\n### Summary\n![Figure created by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/10/1dAggxuYiXtI5iiqGsOgDhw.png)\nFigure created by the author.\n### 2.3. The Naive Bayes Model\nGiven a data matrix **X** and a target vector **y,** we state our problem as:\n![Figure created by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/10/1gvNwsiJ_cTW1_QNIVqDSCg.png)\nFigure created by the author.\nwhere, **y** is **class variable** and **X** is a **dependent feature vector with dimension d i.e. X = (x1,x2,x2, xd),** where **d** is the number of variables/features of the sample.\n  * P(y|X) is the probability of observing the class **y** given the sample **X** with **X = (x1,x2,x2, xd),** where **d** is the number of variables/features of the sample.\n\n\nNow the \"naïve\" [conditional independence](https://en.wikipedia.org/wiki/Conditional_independence) assumptions come into play: assume that all features in **X** are [mutually independent](https://en.wikipedia.org/wiki/Mutually_independent), conditional on the category **y** :\n![Figure created by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/10/1K4RRmFEMKy5aEWlX3WGwFg.png)\nFigure created by the author.\nFinally, to find the probability of a given **sample** for all possible values of the class variable _**y**_ , we just need to find the output with maximum probability:\n![Figure created by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/10/11PtU86RdWWSGyCTGhgXySQ.png)\nFigure created by the author.\n* * *\n## 3. Dealing with text data\nOne question that arises at this point is the following:\n> How are we going to use the raw text data to train the model ? The raw data is a collection of strings !\nText Analysis is a major application field for machine learning algorithms. **However the raw data, a sequence of symbols (i.e. strings) cannot be fed directly to the algorithms themselves as most of them expect numerical feature vectors with a fixed size rather than the raw text documents with variable length.**\nIn order to address this, scikit-learn provides utilities for the most common ways to extract numerical features from text content, namely:\n  * **tokenizing** strings and giving an integer id for each possible token, for instance by using white-spaces and punctuation as token separators.\n  * **counting** the occurrences of tokens in each document.\n\n\nIn this scheme, features and samples are defined as follows:\n  * each **individual token occurrence frequency** is treated as a **feature**.\n  * the vector of all the token frequencies for a given **document** is considered a multivariate **sample**.\n\n\n### \"Counting\" Example (to really understand this before we move on):\ntext\n\n```\nfrom sklearn.feature_extraction.text import CountVectorizer\ncorpus = [\n    'This is the first document.',\n    'This document is the second document.',\n    'And this is the third one.',\n    'Is this the first document?',\n]\n```\n\ntext\n\n```\nvectorizer = CountVectorizer()\nX = vectorizer.fit_transform(corpus)\n```\n\ntext\n\n```\nprint(vectorizer.get_feature_names())\n['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this']\n```\n\ntext\n\n```\nprint(X.toarray())\n[[0 1 1 1 0 0 1 0 1]\n [0 2 0 1 0 1 1 0 1]\n [1 0 0 1 1 0 1 1 1]\n [0 1 1 1 0 0 1 0 1]]\n```\n\nIn the above toy example, we have a collection of strings stored into the variable **corpus.** Using the **text** **transformer** , we can see that we have a specific number of unique strings (vocabulary) in our data.\nThis can be seen by printing the **vectorizer.get_feature_names()** variable**.** We observe that we have **9 unique words**.\nNext, we printed the transformed data (**X**) and **we observe the following** :\n  * We have 4 rows in X as the number of our text strings (**we have the same number of samples after the transformation**).\n  * **We have the same number of columns** (features/variables) in the transformed data (**X**) f**or all the samples (** this was not the case before that transformation i.e. the individual strings had different lengths).\n  * The values 0,1,2, encode the **frequency** of a **word** that **appeared** in the **initial text data.**\n\n\n**E.g**. The first transformed row is **[0 1 1 1 0 0 1 0 1]** and **** th** e unique vocabulary**i** s ['and', 'document', 'first', 'is', 'one', 'second', 'the', 'third', 'this'],** thus this means that the words \"document\", \"first\", \"is\", \"the\" and \"this\" appeared 1 time each in the initial text string (i.e. 'This is the first document.').\n**Side note:** This is the counting approach. The [term-frequency transform](https://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.text.TfidfTransformer.html#sklearn.feature_extraction.text.TfidfTransformer) is nothing more than a transformation of a count matrix into a normalized term-frequency matrix.\nHope everything is clear now. If not, read this paragraph as many times as it is needed in order to really grasp the idea and understand this transformation. **It is a fundamental step.**\n* * *\n## 4. Working example in Python\nNow that you understood how the Naive Bayes and the Text Transformation work, it's time to start coding !\n### Problem Statement\nAs a working example, we will use some **text** **data** and we will build a **Naive** **Bayes** model to **predict** the **categories** of the **texts**. This is a **multi-class (20 classes) text classification problem**.\nLet's start (I will walk you through). First, we will **load all the necessary libraries** :\ntext\n\n```\nimport numpy as np, pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import fetch_20newsgroups\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.metrics import confusion_matrix, accuracy_score\n```\n\ntext\n\n```\nsns.set() # use seaborn plotting style\n```\n\nNext, **let's load the data** (**training** and **test** sets):\ntext\n\n```\n# Load the dataset\ndata = fetch_20newsgroups()\n```\n\ntext\n\n```\n# Get the text categories\ntext_categories = data.target_names\n```\n\ntext\n\n```\n# define the training set\ntrain_data = fetch_20newsgroups(subset=\"train\", categories=text_categories)\n```\n\ntext\n\n```\n# define the test set\ntest_data = fetch_20newsgroups(subset=\"test\", categories=text_categories)\n```\n\nLet's find out **how many classes** and **samples** we have:\ntext\n\n```\nprint(\"We have {} unique classes\".format(len(text_categories)))\nprint(\"We have {} training samples\".format(len(train_data.data)))\nprint(\"We have {} test samples\".format(len(test_data.data)))\n```\n\nThe above prints:\ntext\n\n```\nWe have 20 unique classes\nWe have 11314 training samples\nWe have 7532 test samples\n```\n\nSo, this is a **20-class text classification problem** with n_train = **11314** **training** **samples** (text sentences) and n_test = **7532** **test** **samples** (text sentences).\nLet's visualize the 5th training sample:\ntext\n\n```\n# let's have a look as some training data\nprint(test_data.data[5])\n```\n\nAs mentioned previously, our data are **texts** (more specifically, **emails**) so you should see something like the following printed out:\n![Figure created by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/10/1OkKSVQQNhFE37m-nLcKDAQ.png)\nFigure created by the author.\nThe next step consists of building the **Naive Bayes classifier** and finally **training** the **model.** In our example, we will convert the collection of text documents (train and test sets) into a matrix of token counts (I explained how this works in **Section 3**).\n> To implement that text transformation we will use the **make_pipeline** function. This will internally transform the text data and then the model will be fitted **using the transformed data**.\ntext\n\n```\n# Build the model\nmodel = make_pipeline(TfidfVectorizer(), MultinomialNB())\n```\n\ntext\n\n```\n# Train the model using the training data\nmodel.fit(train_data.data, train_data.target)\n```\n\ntext\n\n```\n# Predict the categories of the test data\npredicted_categories = model.predict(test_data.data)\n```\n\nThe last line of code **predicts the labels of the test set**.\nLet's see the predicted categories names:\ntext\n\n```\nprint(np.array(test_data.target_names)[predicted_categories])\narray(['rec.autos', 'sci.crypt', 'alt.atheism', ..., 'rec.sport.baseball', 'comp.sys.ibm.pc.hardware', 'soc.religion.christian'], dtype='<U24')\n```\n\nFinally, let's build the **multi-class confusion matrix** to see if the model is good or if the model predicts correctly only specific text categories.\ntext\n\n```\n# plot the confusion matrix\nmat = confusion_matrix(test_data.target, predicted_categories)\nsns.heatmap(mat.T, square = True, annot=True, fmt = \"d\", xticklabels=train_data.target_names,yticklabels=train_data.target_names)\nplt.xlabel(\"true labels\")\nplt.ylabel(\"predicted label\")\nplt.show()\n```\n\ntext\n\n```\nprint(\"The accuracy is {}\".format(accuracy_score(test_data.target, predicted_categories)))\n```\n\ntext\n\n```\nThe accuracy is 0.7738980350504514\n```\n\n![Figure created by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2020/10/1eUVx9X8GD9f5IYqaPpMxLQ.png)\nFigure created by the author.\n* * *\n**Note:** If you're passionate about diving deeper into machine learning with Python and sklearn, I highly recommend checking out [**this book**](https://amzn.to/3t4YB2d); it's a game-changer in breaking down complex topics into digestible insights.\n> [**Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques...**](https://amzn.to/3t4YB2d)\n## 5. Bonus: Having fun with the model\nLet's have some fun using the trained model. Let's classify whatever sentence we like 😄 .\ntext\n\n```\n# custom function to have fun\ndef my_predictions(my_sentence, model):\n    all_categories_names = np.array(data.target_names)\n    prediction = model.predict([my_sentence])\n    return all_categories_names[prediction]\n```\n\ntext\n\n```\n\nmy_sentence = \"jesus\"\nprint(my_predictions(my_sentence, model))\n['soc.religion.christian']\n```\n\ntext\n\n```\nmy_sentence = \"Are you an atheist?\"\nprint(my_predictions(my_sentence, model))\n['alt.atheism']\n```\n\nWe inserted the string \"jesus\" to the model and it predicted the class \"['soc.religion.christian']\".\nChange the \"my_sentence\" into other stings and **play with the model** 😃 .\n* * *\n## 6. Conclusions\nWe saw that **Naive Bayes is a very powerful algorithm** for **multi-class text classification problems.**\n_**Side Note**_ : _If you want to know more about the confusion matrix (and the ROC curve) read this:_\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### Interpreting the confusion matrix\nFrom the above **confusion** **matrix** , we can verify that the model is really good.\n  * It is able to correctly predict all 20 classes of the text data (most values are on the diagonal and few are off-the-diagonal).\n  * We also notice that the highest miss-classification (value off-the-diagonal) is **131** (5 lines from the end, last column at the right). The value 131 means that 131 documents that belonged to the \"**religion miscellaneous** \" category were miss-classified as belonging to the \"**religion christian** \" category.\n\n\nInteresting thing that these 2 categories are really similar and actually one could characterize these as 2 subgroups of a larger group e.g. \"religion\" in general.\nFinally, the **accuracy** on the **test** set is **0.7739** which is quite good for a **20-class text classification problem** 🚀 .\n**That's all folks! Hope you liked this article.**\n* * *\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### My Profile (have a look to find out about my collection of articles):\n> [**Serafeim Loukas - Towards Data Science**](https://towardsdatascience.com/@seralouk)\n* * *\n## Get in touch with me\n  * **LinkedIn** : <https://www.linkedin.com/in/serafeim-loukas/>\n\n\n* * *\n### You may also like:\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> [**K-Means Clustering: How It Works & Finding The Optimum Number Of Clusters In The Data**](https://towardsdatascience.com/k-means-clustering-how-it-works-finding-the-optimum-number-of-clusters-in-the-data-13d18739255c)\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> [**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## 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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