{"id":177192,"date":"2024-01-21T10:34:17","date_gmt":"2024-01-21T09:34:17","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=177192"},"modified":"2024-01-21T10:43:40","modified_gmt":"2024-01-21T09:43:40","slug":"train_test_split-tutorial-on-how-to-use-this-function","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function","title":{"rendered":"&#8220;train_test_split: Tutorial on how to use this function"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"177192\" class=\"elementor elementor-177192\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-64a2e1f e-flex e-con-boxed e-con\" data-id=\"64a2e1f\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-5ca1278 elementor-widget elementor-widget-heading\" data-id=\"5ca1278\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">A Machine Learning model is capable of learning autonomously from one dataset, with the aim of predicting behavior on another dataset. To do this, it finds underlying relationships between independent explanatory variables and a target variable in the initial dataset. It then uses these patterns to predict or classify new data. <\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-bd43774 e-flex e-con-boxed e-con\" data-id=\"bd43774\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a2e4bfd elementor-widget elementor-widget-heading\" data-id=\"a2e4bfd\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How do I define the train_test_split function?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-34cd857 elementor-widget elementor-widget-text-editor\" data-id=\"34cd857\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>To verify the effectiveness of a <a href=\"https:\/\/datascientest.com\/en\/automl-and-machine-learning-automation-a-threat-to-data-scientists\">Machine Learning model,<\/a> the initial dataset is divided into two sets: a training set and a test set. The training set is used to fit, i.e. train, the model on part of the data. The test set is used to evaluate the model&#8217;s performance on the other part of the data. The train_test_split function in <a href=\"https:\/\/datascientest.com\/en\/scikit-learn-discover-the-python-library-dedicated-to-machine-learning\">Python&#8217;s ScikitLearn (sklearn) library allows this separation into two sets.<\/a><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ae289a7 elementor-widget elementor-widget-text-editor\" data-id=\"ae289a7\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>First of all, you need to import the train_test_split function from sklearn&#8217;s model_selection package using the following code:<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-745707d elementor-widget elementor-widget-shortcode\" data-id=\"745707d\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/manongeorget\/53cdea38bb6406ea326f450d6a332b9c.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-363f0dd elementor-widget elementor-widget-text-editor\" data-id=\"363f0dd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Once imported, the <strong>function takes several arguments:<\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e0e8ad4 elementor-widget elementor-widget-image\" data-id=\"e0e8ad4\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t<figure class=\"wp-caption\">\n\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/datascientest.com\/wp-content\/uploads\/2023\/03\/train-test-split1.png\" title=\"\" alt=\"\" loading=\"lazy\" \/>\t\t\t\t\t\t\t\t\t\t\t<figcaption class=\"widget-image-caption wp-caption-text\"><\/figcaption>\n\t\t\t\t\t\t\t\t\t\t<\/figure>\n\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b97596d elementor-widget elementor-widget-heading\" data-id=\"b97596d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">1) Arrays extracted from the dataset to be split. <\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6f7fe06 elementor-widget elementor-widget-text-editor\" data-id=\"6f7fe06\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>In <strong>supervised learning<\/strong>, these arrays are the input array X, consisting of the explanatory variables in columns, and the output array y, consisting of the target variable (i.e. the labels).<\/p><p>In unsupervised learning, the only array in argument is the input array X, made up of the explanatory variables in columns.<\/p><p><span style=\"text-decoration: underline;\"><strong>Note:<\/strong><\/span> Be careful with dimensions! X must be a two-dimensional array. y must be a one-dimensional array equal to the number of rows in X. To do this, use the .reshape function.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8fd7a0b elementor-widget elementor-widget-heading\" data-id=\"8fd7a0b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">2) Test set size (test_size) and training set size (train_size).<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8961e4d elementor-widget elementor-widget-text-editor\" data-id=\"8961e4d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The<strong> size of each set<\/strong> is either a decimal number between 0 and 1 representing a <a href=\"https:\/\/datascientest.com\/en\/datasets-top-5-places-to-find-quality-datasets\">proportion of the dataset<\/a>, or an integer number representing a number of examples in the dataset.<\/p><p><strong>Note:<\/strong> It is sufficient to define just one of these arguments, the second being complementary.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5fb7d4b elementor-widget elementor-widget-heading\" data-id=\"5fb7d4b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">3) The random state (random_state).<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-51e8e10 elementor-widget elementor-widget-text-editor\" data-id=\"51e8e10\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The random state is a number that controls how the<strong> pseudo-random generator divides the data.<\/strong><\/p><p><span style=\"text-decoration: underline;\"><strong>Note<\/strong><\/span>: Choosing an integer as the random state ensures that the data is split in the same way each time the function is called. This makes the code reproducible.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-72eda6f elementor-widget elementor-widget-heading\" data-id=\"72eda6f\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">4) Le shuffle (shuffle).<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6ffdd69 elementor-widget elementor-widget-text-editor\" data-id=\"6ffdd69\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Shuffle is a Boolean that selects whether or not data should be shuffled before being separated. If the data is not shuffled, it is separated according to the order in which it was initially displayed.<\/p><p><span style=\"text-decoration: underline;\"><strong>Note:<\/strong><\/span> The default value is True.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-20a16c7 elementor-widget elementor-widget-heading\" data-id=\"20a16c7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">5) Stratify (stratify).<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-884c738 elementor-widget elementor-widget-text-editor\" data-id=\"884c738\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The Stratify parameter selects whether the <strong>data are separated<\/strong> so as to keep the same <a href=\"https:\/\/datascientest.com\/en\/excel-to-power-bi-how-to-transform-a-pivot-table-in-excel-into-a-dataset-that-can-be-used-by-power-bi\">proportions of observations in each class in the train and test sets as in the initial dataset.<\/a><\/p><p><strong>Remarks:<\/strong><\/p><ul><li>This parameter is particularly useful when dealing with &#8220;unbalanced&#8221; data with very unbalanced proportions between the different classes.<\/li><li>The default value is None.<\/li><\/ul>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5f2251e elementor-widget elementor-widget-text-editor\" data-id=\"5f2251e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>The<strong> train_test_split function<\/strong> returns a number of outputs equal to twice its number of inputs, in array form. In supervised learning, it returns four outputs: X_train, X_test, y_train and y_test. In unsupervised learning, it returns two outputs: <strong>X_train and X_test.<\/strong><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4112472 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"4112472\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/machine-learning-engineer-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Learn to use Machine Laarning models<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-351c112 e-flex e-con-boxed e-con\" data-id=\"351c112\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-70865d4 elementor-widget elementor-widget-heading\" data-id=\"70865d4\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How to evaluate model performance with the train_test_split function?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d075f24 elementor-widget elementor-widget-text-editor\" data-id=\"d075f24\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Once the<strong> train_test_split function<\/strong> has been defined, it returns a train set and a test set. This splitting of the data makes it possible to<a href=\"https:\/\/datascientest.com\/en\/cracking-the-code-on-underfitting-in-machine-learning\"> evaluate a Machine Learning model from two different angles.<\/a><\/p><p>The model is trained on the train set returned by the function. Then its predictive capabilities are evaluated on the test set returned by the function. Several metrics can be used for this evaluation. In the case of linear regression, the coefficient of determination,<strong> RMSE and MAE are preferred.<\/strong> In the <a href=\"https:\/\/datascientest.com\/en\/classification-algorithms-definition-and-main-models\">case of classification,<\/a> accuracy, precision, recall and F1-score are preferred. These scores on the test set are therefore used to determine how well the model performs and how much it needs to be improved before it can predict on a new dataset.<\/p><p>The train and test sets returned by the train_test_split function also play an essential role in <a href=\"https:\/\/datascientest.com\/en\/overfitting-what-is-it-how-can-i-avoid-it\">detecting overfitting<\/a> or underfitting. As a reminder, overfitting (or overlearning) describes a situation where the model built is too complex (with too many explanatory variables, for example), such that it learns the training data perfectly but fails to generalize to other data.<\/p><p>Conversely,<strong> underfitting (or underlearning)<\/strong> describes a situation where the model is too simple or poorly chosen (choosing a linear regression on data that does not respect its assumptions, for example), such that it learns poorly. These two problems can be corrected by various techniques, but they must first be identified, which is made possible by the train_test_split function.<\/p><p>In fact, we can compare the model&#8217;s performance on the train set and the test set created by the function. If performance is good on the train set but poor on the test set, we&#8217;re probably dealing with overfitting. If performance is as bad on the train set as on the test set, we&#8217;re probably <a href=\"https:\/\/datascientest.com\/en\/cracking-the-code-on-underfitting-in-machine-learning\">dealing with underfitting<\/a>. The two sets returned by the function are therefore essential in detecting these recurring problems in Machine Learning.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0cd9fe8 e-flex e-con-boxed e-con\" data-id=\"0cd9fe8\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-dd32bad elementor-widget elementor-widget-heading\" data-id=\"dd32bad\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">How can I solve a complete Machine Learning problem using the train_test_split function?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a5e7c0b elementor-widget elementor-widget-text-editor\" data-id=\"a5e7c0b\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Now that we&#8217;ve understood the use and functionality of the train_test_split function, let&#8217;s put it into practice with a real Machine Learning problem.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-71ef9e1 elementor-widget elementor-widget-heading\" data-id=\"71ef9e1\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Step 1: Understanding the problem<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8a1adab elementor-widget elementor-widget-text-editor\" data-id=\"8a1adab\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>We choose to solve a supervised learning problem where the expected labels are known. More precisely, we focus on binary classification. The objective is to predict whether or not an individual has breast cancer, based on body characteristics.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3fc5098 elementor-widget elementor-widget-heading\" data-id=\"3fc5098\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Step 2: Data recovery<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-36fd830 elementor-widget elementor-widget-text-editor\" data-id=\"36fd830\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>We use the &#8220;breast_cancer&#8221; dataset included in the Sklearn library.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-db3e83a elementor-widget elementor-widget-shortcode\" data-id=\"db3e83a\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/manongeorget\/70ef7bb2343550b5656aec87400c31e7.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fe7d7c2 elementor-widget elementor-widget-text-editor\" data-id=\"fe7d7c2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>With the following lines of code, we retrieve the features and the target variable:<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3e4b423 elementor-widget elementor-widget-shortcode\" data-id=\"3e4b423\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/manongeorget\/7c4b377a2e95b5b76ed6427f0deedc3c.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-e708124 elementor-widget elementor-widget-text-editor\" data-id=\"e708124\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>We obtain that the target variable to be predicted takes two values (&#8220;malignant&#8221; and &#8220;benign&#8221;) and that the problem is indeed a binary classification.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3bd825c elementor-widget elementor-widget-heading\" data-id=\"3bd825c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Step 3: Creating X and y<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7314735 elementor-widget elementor-widget-text-editor\" data-id=\"7314735\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>We create a<strong> two-dimensional input array X<\/strong> and a one-dimensional output array y. For this dataset, the binary encoding of the target variable is performed by sklearn and can be retrieved directly.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cedb770 elementor-widget elementor-widget-shortcode\" data-id=\"cedb770\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/manongeorget\/746ed23fb3f50d3fca325730eb9f09bc.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d6517ad elementor-widget elementor-widget-text-editor\" data-id=\"d6517ad\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>We check that the dimensions of X and y match: y has the same number of rows as X.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ce9461a elementor-widget elementor-widget-heading\" data-id=\"ce9461a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Step 4: Creating train and test assemblies<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f94d91c elementor-widget elementor-widget-text-editor\" data-id=\"f94d91c\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>We divide the data into a train set and a test set.<\/p><p>Since we supply the train_test_split function with two arrays X and y, it returns four elements. We choose a test set consisting of 10% of the data. We choose a number of type &#8220;int&#8221; as the random state to ensure code reproducibility. We don&#8217;t use the function&#8217;s final parameters, which are unnecessary for such a simple problem.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-3301100 elementor-widget elementor-widget-shortcode\" data-id=\"3301100\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/manongeorget\/670c9b5bc2be84da42c62628e30d0b52.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-160583b elementor-widget elementor-widget-heading\" data-id=\"160583b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Step 5: Classification model<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2ff9e01 elementor-widget elementor-widget-text-editor\" data-id=\"2ff9e01\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>To solve the classification task, we build a k-nearest neighbor model. We train the model on the train set using the .fit() method. Then we test the model&#8217;s performance on the test set with the .predict() method. This gives us the predicted classes for the observations in the test set.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bc00130 elementor-widget elementor-widget-shortcode\" data-id=\"bc00130\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/manongeorget\/9b6f1804e4371bee273426fb19d5af8e.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c66206a elementor-widget elementor-widget-heading\" data-id=\"c66206a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h4 class=\"elementor-heading-title elementor-size-default\">Step 6: Model evaluation<\/h4>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5242505 elementor-widget elementor-widget-text-editor\" data-id=\"5242505\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>We choose accuracy as our metric. Accuracy represents the number of correct predictions out of the total number of predictions. We calculate it on the train set and on the test set using the .score() method, which compares the real classes in the dataset with the classes predicted by the clf classifier.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-58892d9 elementor-widget elementor-widget-shortcode\" data-id=\"58892d9\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-shortcode\"><script src=\"https:\/\/gist.github.com\/manongeorget\/0b4ae518863c3e024bcb11bc1d31b96d.js\"><\/script><\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-14d4fa3 elementor-widget elementor-widget-text-editor\" data-id=\"14d4fa3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>We obtain an accuracy of 0.95 on the train set and 0.93 on the test set. So the model has good classification performance.<\/p><p><br \/>What&#8217;s more, the accuracy on the test set is very slightly lower than that on the train set. This means that the model generalizes well to new data. So we&#8217;re not facing an overfitting problem.<\/p><p>So the train_test_split function is easy to use and highly effective in solving a complete Machine Learning problem.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-9f78ccf e-flex e-con-boxed e-con\" data-id=\"9f78ccf\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-426bfd3 elementor-widget elementor-widget-heading\" data-id=\"426bfd3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Are there any limits to the train_test_split function?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0301b3e elementor-widget elementor-widget-text-editor\" data-id=\"0301b3e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Despite this, the train_test_split function has a major limitation linked to its random_state parameter. In fact, when the value given to random state is an integer, the data are separated by a pseudo-random generator initialized with this integer, called seed.<\/p><p>The separation performed is reproducible by keeping the same seed. However, it has been shown that the choice of seed has an influence on the performance of the <a href=\"https:\/\/datascientest.com\/en\/boosting-business-with-3-essential-machine-learning-algorithms\">associated Machine Learning model<\/a>: different seeds can create different sets and variable scores.<\/p><p>One solution to this problem is to use the train_test_split function several times with different values for the random_state. We can then calculate the average of the scores obtained.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8310f58 elementor-widget elementor-widget-text-editor\" data-id=\"8310f58\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Thus the train_test_split function in the sklearn Python library is essential for conducting a Data Science project and evaluating a Machine Learning model when used properly !<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-669b9bc elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"669b9bc\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/en\/data-scientist-course\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Discover our Data Scientist training<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-e577c72 e-flex e-con-boxed e-con\" data-id=\"e577c72\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-a67709c elementor-widget elementor-widget-html\" data-id=\"a67709c\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\n{\n  \"@context\": \"https:\/\/schema.org\",\n  \"@type\": \"FAQPage\",\n  \"mainEntity\": [{\n    \"@type\": \"Question\",\n    \"name\": \"How do I define the train_test_split function?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"In order to verify the effectiveness of a Machine Learning model, the initial dataset is divided into two sets: a training set and a test set. The training set is used to fit, i.e. train, the model on part of the data. The test set is used to evaluate the model's performance on the other part of the data. The train_test_split function in Python's ScikitLearn (sklearn) library allows this separation into two sets.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"How to evaluate the performance of a model with the train_test_split function?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Once the train_test_split function has been defined, it returns a train set and a test set. This splitting of the data makes it possible to evaluate a Machine Learning model from two different angles. The model is trained on the train set returned by the function. Then its predictive capabilities are evaluated on the test set returned by the function. Several metrics can be used for this evaluation. In the case of linear regression, the coefficient of determination, RMSE and MAE are preferred. In the case of classification, accuracy, precision, recall and F1-score are preferred. These scores on the test set are therefore used to determine how well the model performs and how much it needs to be improved before it can predict on a new dataset.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"How can I solve a complete Machine Learning problem using the train_test_split function?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"We choose to solve a supervised learning problem such that the expected labels are known. More precisely, we focus on binary classification. The objective is to predict whether or not an individual has breast cancer, based on his or her body characteristics.\"\n    }\n  },{\n    \"@type\": \"Question\",\n    \"name\": \"Are there any limits to the train_test_split function?\",\n    \"acceptedAnswer\": {\n      \"@type\": \"Answer\",\n      \"text\": \"Despite this, the train_test_split function has a major limitation linked to its random_state parameter. In fact, when the value given to the random state is an integer, the data are separated by a pseudo-random generator initialized with this integer, called a seed. The separation performed is reproducible by keeping the same seed. However, it has been shown that the choice of seed has an influence on the performance of the associated Machine Learning model: different seeds can create different sets and variable scores. One solution to this problem is to use the train_test_split function several times with different values for the random_state. We can then calculate the average of the scores obtained.\"\n    }\n  }]\n}\n<\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>A Machine Learning model is capable of learning autonomously from one dataset, with the aim of predicting behavior on another dataset. To do this, it finds underlying relationships between independent explanatory variables and a target variable in the initial dataset. It then uses these patterns to predict or classify new data. How do I define [&hellip;]<\/p>\n","protected":false},"author":67,"featured_media":177193,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>&quot;train_test_split: Tutorial on how to use this function<\/title>\n<meta name=\"description\" content=\"Master the art of data splitting with our comprehensive tutorial on train_test_split. Learn how to effectively use this function in Python, a crucial tool for creating training and testing datasets, optimizing machine learning model evaluation, and enhancing the robustness of your predictive models.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function\" \/>\n<meta property=\"og:locale\" content=\"en_GB\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"&quot;train_test_split: Tutorial on how to use this function\" \/>\n<meta property=\"og:description\" content=\"A Machine Learning model is capable of learning autonomously from one dataset, with the aim of predicting behavior on another dataset. To do this, it\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function\" \/>\n<meta property=\"og:site_name\" content=\"Data Science Courses | DataScientest\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/DataScientestEN\" \/>\n<meta property=\"article:published_time\" content=\"2024-01-21T09:34:17+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-01-21T09:43:40+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/en\/files\/2024\/01\/train-test-split.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"512\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Melanie\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:site\" content=\"@DataScientest__\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Melanie\" \/>\n\t<meta name=\"twitter:label2\" content=\"Estimated reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function#article\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function\"},\"author\":{\"name\":\"Melanie\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d\"},\"headline\":\"&#8220;train_test_split: Tutorial on how to use this function\",\"datePublished\":\"2024-01-21T09:34:17+00:00\",\"dateModified\":\"2024-01-21T09:43:40+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function\"},\"wordCount\":1471,\"commentCount\":0,\"publisher\":{\"@id\":\"https:\/\/datascientest.com\/en\/#organization\"},\"articleSection\":[\"Data Science\"],\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"CommentAction\",\"name\":\"Comment\",\"target\":[\"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function#respond\"]}],\"copyrightYear\":\"2024\",\"copyrightHolder\":{\"@id\":\"https:\/\/datascientest.com\/#organization\"}},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function\",\"url\":\"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function\",\"name\":\"\\\"train_test_split: Tutorial on how to use this function\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/en\/#website\"},\"datePublished\":\"2024-01-21T09:34:17+00:00\",\"dateModified\":\"2024-01-21T09:43:40+00:00\",\"description\":\"Master the art of data splitting with our comprehensive tutorial on train_test_split. Learn how to effectively use this function in Python, a crucial tool for creating training and testing datasets, optimizing machine learning model evaluation, and enhancing the robustness of your predictive models.\",\"inLanguage\":\"en-GB\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function\"]}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/datascientest.com\/en\/#website\",\"url\":\"https:\/\/datascientest.com\/en\/\",\"name\":\"Data Science Courses | DataScientest\",\"description\":\"Leader in Data Science Courses\",\"publisher\":{\"@id\":\"https:\/\/datascientest.com\/en\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/datascientest.com\/en\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-GB\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/datascientest.com\/en\/#organization\",\"name\":\"DataScientest\",\"url\":\"https:\/\/datascientest.com\/en\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png\",\"contentUrl\":\"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png\",\"width\":301,\"height\":301,\"caption\":\"DataScientest\"},\"image\":{\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/\"},\"sameAs\":[\"https:\/\/www.facebook.com\/DataScientestEN\",\"https:\/\/twitter.com\/DataScientest__\",\"https:\/\/www.linkedin.com\/school\/datascientest\/mycompany\/\",\"https:\/\/www.tiktok.com\/@datascientest\"]},{\"@type\":\"Person\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d\",\"name\":\"Melanie\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-GB\",\"@id\":\"https:\/\/datascientest.com\/en\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g\",\"caption\":\"Melanie\"},\"url\":\"https:\/\/datascientest.com\/en\/author\/melaniekrause\"}]}<\/script>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"\"train_test_split: Tutorial on how to use this function","description":"Master the art of data splitting with our comprehensive tutorial on train_test_split. Learn how to effectively use this function in Python, a crucial tool for creating training and testing datasets, optimizing machine learning model evaluation, and enhancing the robustness of your predictive models.","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function","og_locale":"en_GB","og_type":"article","og_title":"\"train_test_split: Tutorial on how to use this function","og_description":"A Machine Learning model is capable of learning autonomously from one dataset, with the aim of predicting behavior on another dataset. To do this, it","og_url":"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function","og_site_name":"Data Science Courses | DataScientest","article_publisher":"https:\/\/www.facebook.com\/DataScientestEN","article_published_time":"2024-01-21T09:34:17+00:00","article_modified_time":"2024-01-21T09:43:40+00:00","og_image":[{"width":1024,"height":512,"url":"https:\/\/datascientest.com\/en\/files\/2024\/01\/train-test-split.webp","type":"image\/webp"}],"author":"Melanie","twitter_card":"summary_large_image","twitter_creator":"@DataScientest__","twitter_site":"@DataScientest__","twitter_misc":{"Written by":"Melanie","Estimated reading time":"7 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"Article","@id":"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function#article","isPartOf":{"@id":"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function"},"author":{"name":"Melanie","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d"},"headline":"&#8220;train_test_split: Tutorial on how to use this function","datePublished":"2024-01-21T09:34:17+00:00","dateModified":"2024-01-21T09:43:40+00:00","mainEntityOfPage":{"@id":"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function"},"wordCount":1471,"commentCount":0,"publisher":{"@id":"https:\/\/datascientest.com\/en\/#organization"},"articleSection":["Data Science"],"inLanguage":"en-GB","potentialAction":[{"@type":"CommentAction","name":"Comment","target":["https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function#respond"]}],"copyrightYear":"2024","copyrightHolder":{"@id":"https:\/\/datascientest.com\/#organization"}},{"@type":"WebPage","@id":"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function","url":"https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function","name":"\"train_test_split: Tutorial on how to use this function","isPartOf":{"@id":"https:\/\/datascientest.com\/en\/#website"},"datePublished":"2024-01-21T09:34:17+00:00","dateModified":"2024-01-21T09:43:40+00:00","description":"Master the art of data splitting with our comprehensive tutorial on train_test_split. Learn how to effectively use this function in Python, a crucial tool for creating training and testing datasets, optimizing machine learning model evaluation, and enhancing the robustness of your predictive models.","inLanguage":"en-GB","potentialAction":[{"@type":"ReadAction","target":["https:\/\/datascientest.com\/en\/train_test_split-tutorial-on-how-to-use-this-function"]}]},{"@type":"WebSite","@id":"https:\/\/datascientest.com\/en\/#website","url":"https:\/\/datascientest.com\/en\/","name":"Data Science Courses | DataScientest","description":"Leader in Data Science Courses","publisher":{"@id":"https:\/\/datascientest.com\/en\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/datascientest.com\/en\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-GB"},{"@type":"Organization","@id":"https:\/\/datascientest.com\/en\/#organization","name":"DataScientest","url":"https:\/\/datascientest.com\/en\/","logo":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/","url":"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png","contentUrl":"https:\/\/datascientest.com\/en\/wp-content\/blogs.dir\/9\/files\/2024\/02\/DataScientest_icon_blue-2.png","width":301,"height":301,"caption":"DataScientest"},"image":{"@id":"https:\/\/datascientest.com\/en\/#\/schema\/logo\/image\/"},"sameAs":["https:\/\/www.facebook.com\/DataScientestEN","https:\/\/twitter.com\/DataScientest__","https:\/\/www.linkedin.com\/school\/datascientest\/mycompany\/","https:\/\/www.tiktok.com\/@datascientest"]},{"@type":"Person","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/19d5f7ade9407eb69af3bf6d1ae1731d","name":"Melanie","image":{"@type":"ImageObject","inLanguage":"en-GB","@id":"https:\/\/datascientest.com\/en\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/8d3d660a0698de06f13ecf4823519da1?s=96&d=mm&r=g","caption":"Melanie"},"url":"https:\/\/datascientest.com\/en\/author\/melaniekrause"}]}},"_links":{"self":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts\/177192"}],"collection":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/users\/67"}],"replies":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/comments?post=177192"}],"version-history":[{"count":0,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/posts\/177192\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/media\/177193"}],"wp:attachment":[{"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/media?parent=177192"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/categories?post=177192"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascientest.com\/en\/wp-json\/wp\/v2\/tags?post=177192"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}