{"id":25545913,"date":"2022-11-05T15:55:59","date_gmt":"2022-11-05T10:25:59","guid":{"rendered":"https:\/\/entri.app\/blog\/?p=25545913"},"modified":"2023-11-23T16:45:38","modified_gmt":"2023-11-23T11:15:38","slug":"random-forest-regression-in-python","status":"publish","type":"post","link":"https:\/\/entri.app\/blog\/random-forest-regression-in-python\/","title":{"rendered":"Random Forest Regression in Python"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_79_2 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69e1131cc0e6b\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69e1131cc0e6b\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/entri.app\/blog\/random-forest-regression-in-python\/#What_is_Random_Forest\" >What is Random Forest?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/entri.app\/blog\/random-forest-regression-in-python\/#Advantages_and_disadvantages\" >Advantages and disadvantages<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/entri.app\/blog\/random-forest-regression-in-python\/#When_to_use_Random_Forest_in_real_life\" >When to use Random Forest in real life<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/entri.app\/blog\/random-forest-regression-in-python\/#Final_Thoughts\" >Final Thoughts<\/a><\/li><\/ul><\/nav><\/div>\n<div>\n<div class=\"article-title\"><\/div>\n<\/div>\n<div class=\"text\">\n<p>Every decision tree has high variance, but when we combine all of them together in parallel then the resultant variance is low as each decision tree gets perfectly trained on that particular sample data, and hence the output doesn\u2019t depend on one decision tree but on multiple decision trees. In the case of a classification problem, the final output is taken by using the majority voting classifier. In the case of a regression problem, the final output is the mean of all the outputs. This part is called\u00a0<strong>Aggregation<\/strong>.<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/media.geeksforgeeks.org\/wp-content\/uploads\/20200516180708\/Capture482.png\" alt=\"\" \/><\/p>\n<p>Random Forest is an ensemble technique capable of performing both regression and classification tasks with the use of multiple decision trees and a technique called Bootstrap and Aggregation, commonly known as\u00a0<strong>bagging<\/strong>. The basic idea behind this is to combine multiple decision trees in determining the final output rather than relying on individual decision trees.<\/p>\n<div class=\"lead-gen-block\"><a href=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/03\/Python_PDF.pdf\" data-url=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/03\/Python_PDF.pdf\" class=\"lead-pdf-download\" data-id=\"&lt;strong&gt;25556851&lt;\/strong&gt;\">\n<p style=\"text-align: center;\"><strong>Download Python Programming Course Syllabus! <\/a><\/div><\/strong><\/p>\n<p>Random Forest has multiple decision trees as base learning models. We randomly perform row sampling and feature sampling from the dataset forming sample datasets for every model. This part is called Bootstrap.<\/p>\n<p style=\"text-align: center;\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/course\/python-programming-course\/\" target=\"_blank\" rel=\"noopener\">&#8220;Ready to take your python skills to the next level? Sign up for a free demo today!&#8221;<\/a><\/strong><\/p>\n<p>We need to approach the Random Forest regression technique like any other machine learning technique<\/p>\n<ul>\n<li>Design a specific question or data and get the source to determine the required data.<\/li>\n<li>Make sure the data is in an accessible format else convert it to the required format.<\/li>\n<li>Specify all noticeable anomalies and missing data points that may be required to achieve the required data.<\/li>\n<li>Create a machine learning model<\/li>\n<li>Set the baseline model that you want to achieve<\/li>\n<li>Train the data machine learning model.<\/li>\n<\/ul>\n<p><a href=\"https:\/\/entri.app\/course\/python-programming-course\/\" target=\"_blank\" rel=\"noopener\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-25520910 size-full\" src=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square.png\" alt=\"Python and Machine Learning Square\" width=\"345\" height=\"345\" srcset=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square.png 345w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-300x300.png 300w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-150x150.png 150w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-24x24.png 24w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-48x48.png 48w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-96x96.png 96w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-75x75.png 75w\" sizes=\"auto, (max-width: 345px) 100vw, 345px\" \/><\/a><\/p>\n<p style=\"text-align: center;\" data-selectable-paragraph=\"\"><a class=\"btn btn-default\" href=\"https:\/\/entri.app\/course\/python-programming-course\/\">be a python programmer ! learn from the best !<\/a><\/p>\n<p id=\"ca6f\" class=\"pw-post-body-paragraph ld le iy lf b lg lh jz li lj lk kc ll lm ln lo lp lq lr ls lt lu lv lw lx ly ir ga\" data-selectable-paragraph=\"\"><strong class=\"lf iz\">Definitions:<br \/>\nDecision Trees<\/strong>\u00a0are used for both regression and classification problems. They visually flow like trees, hence the name, and in the regression case, they start with the root of the tree and follow splits based on variable outcomes until a leaf node is reached and the result is given. An example of a decision tree is below:<\/p>\n<figure class=\"ko kp kq kr gx ks gl gm paragraph-image\">\n<div class=\"kt ku do kv ce kw\" role=\"button\">\n<div class=\"gl gm lz\"><img loading=\"lazy\" decoding=\"async\" class=\"ce kx ky c\" role=\"presentation\" src=\"https:\/\/miro.medium.com\/max\/700\/1*LjVAVEV4UYYsPMSsjDhkuQ.png\" alt=\"\" width=\"700\" height=\"527\" \/><\/div>\n<\/div>\n<\/figure>\n<p id=\"d156\" class=\"pw-post-body-paragraph ld le iy lf b lg lh jz li lj lk kc ll lm ln lo lp lq lr ls lt lu lv lw lx ly ir ga\" data-selectable-paragraph=\"\">Here we see a basic decision tree diagram which starts with the Var_1 and splits based off of specific criteria. When \u2018yes\u2019, the decision tree follows the represented path, when \u2018no\u2019, the decision tree goes down the other path. This process repeats until the decision tree reaches the leaf node and the resulting outcome is decided. For the example above, the values of a, b, c, or d could be representative of any numeric or categorical value.<\/p>\n<p><strong><div class=\"lead-gen-block\"><a href=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2023\/05\/1_merged-3_compressed.pdf\" data-url=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2023\/05\/1_merged-3_compressed.pdf\" class=\"lead-pdf-download\" data-id=\"25556851\"><\/strong><\/p>\n<p style=\"text-align: center;\"><button class=\"btn btn-default\">Free SQL Tutorial for Beginners &#8211; Download PDF<\/button><\/p>\n<p><strong><\/a><\/div><\/strong><\/p>\n<p data-selectable-paragraph=\"\"><a href=\"https:\/\/entri.app\/course\/python-programming-course\/\" target=\"_blank\" rel=\"noopener\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-25520910 size-full\" src=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square.png\" alt=\"Python and Machine Learning Square\" width=\"345\" height=\"345\" srcset=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square.png 345w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-300x300.png 300w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-150x150.png 150w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-24x24.png 24w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-48x48.png 48w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-96x96.png 96w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Square-75x75.png 75w\" sizes=\"auto, (max-width: 345px) 100vw, 345px\" \/><\/a><\/p>\n<div class=\"responsive-tabs-wrapper\">\n<div class=\"responsive-tabs responsive-tabs--enabled\">\n<div id=\"tablist5-panel1\" class=\"tabcontent responsive-tabs__panel responsive-tabs__panel--active\" role=\"tabpanel\" aria-hidden=\"false\" aria-labelledby=\"tablist5-tab1\">\n<div class=\"code-block\">\n<div class=\"code-gutter\">\n<div class=\"elementor-element elementor-element-347440b elementor-widget elementor-widget-heading\" data-id=\"347440b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n<div class=\"elementor-widget-container\">\n<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"What_is_Random_Forest\"><\/span><strong>What is Random Forest?<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-3a89e17 elementor-widget elementor-widget-text-editor\" data-id=\"3a89e17\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n<div class=\"elementor-widget-container\">\n<p>Random Forest is a Supervised learning algorithm that is based on the ensemble learning method and many Decision Trees. Random Forest is a Bagging technique, so all calculations are run in parallel and there is no interaction between the Decision Trees when building them. RF can be used to solve both Classification and Regression tasks.<\/p>\n<p>The name \u201cRandom Forest\u201d comes from the Bagging idea of data randomization (Random) and building multiple Decision Trees (Forest). Overall, it is a powerful ML algorithm that limits the disadvantages of a Decision Tree model.<\/p>\n<h4 style=\"text-align: center;\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/course\/python-programming-course\/\" target=\"_blank\" rel=\"noopener\">&#8220;Experience the power of our web development course with a free demo &#8211; enroll now!&#8221;<\/a><\/strong><\/h4>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-5e229da elementor-widget elementor-widget-heading\" data-id=\"5e229da\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n<div class=\"elementor-widget-container\">\n<h3 class=\"elementor-heading-title elementor-size-default\"><strong>Random Forest Algorithm<\/strong><\/h3>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-5bcd8a0 elementor-widget elementor-widget-text-editor\" data-id=\"5bcd8a0\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n<div class=\"elementor-widget-container\">\n<p>To make things clear let\u2019s take a look at the exact algorithm of the Random Forest:<\/p>\n<ol>\n<li>So, you have your original dataset D, you want to have K Decision Trees in our ensemble. Additionally, you have a number N \u2013 you will build a Tree until there are less or equal to N samples in each node (for the Regression, task N is usually equal to 5). Moreover, you have a number F \u2013 number of features that will be randomly selected in each node of the Decision Tree. The feature that will be used to split the node is picked from these F features (for the Regression task, F is usually equal to sqrt (number of features of the original dataset D)<\/li>\n<li>Everything else is rather simple. Random Forest creates K subsets of the data from the original dataset D. Samples that do not appear in any subset are called \u201cout-of-bag\u201d samples.<\/li>\n<li>K trees are built using a single subset only. Also, each tree is built until there are fewer or equal to N samples in each node. Moreover, in each node F features are randomly selected. One of them is used to split the node<\/li>\n<li>K trained models form an ensemble and the final result for the Regression task is produced by averaging the predictions of the individual trees<\/li>\n<\/ol>\n<p>In the picture below you might see the Random Forest algorithm for Classification.<\/p>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-44faba3 elementor-widget elementor-widget-image\" data-id=\"44faba3\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n<div class=\"elementor-widget-container\"><img loading=\"lazy\" decoding=\"async\" class=\"attachment-large size-large\" src=\"https:\/\/cnvrg.io\/wp-content\/uploads\/2021\/02\/Random-Forest-Algorithm-1024x576.jpg\" srcset=\"https:\/\/cnvrg.io\/wp-content\/uploads\/2021\/02\/Random-Forest-Algorithm-1024x576.jpg 1024w, https:\/\/cnvrg.io\/wp-content\/uploads\/2021\/02\/Random-Forest-Algorithm-300x169.jpg 300w, https:\/\/cnvrg.io\/wp-content\/uploads\/2021\/02\/Random-Forest-Algorithm-768x432.jpg 768w, https:\/\/cnvrg.io\/wp-content\/uploads\/2021\/02\/Random-Forest-Algorithm-1536x864.jpg 1536w, https:\/\/cnvrg.io\/wp-content\/uploads\/2021\/02\/Random-Forest-Algorithm.jpg 1600w\" alt=\"\" width=\"800\" height=\"450\" \/><\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-8aa3505 elementor-widget elementor-widget-heading\" data-id=\"8aa3505\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n<div class=\"elementor-widget-container\">\n<table>\n<tbody>\n<tr>\n<td style=\"text-align: center;\" colspan=\"3\">\n<h5><span style=\"color: #ffffff;\"><strong>Are you aspiring for a booming career in IT? If YES, then dive in<\/strong><\/span><\/h5>\n<\/td>\n<\/tr>\n<tr>\n<td>\n<h5><a href=\"https:\/\/entri.app\/course\/full-stack-developer-course\/\"><strong>Full Stack Developer Course<\/strong><\/a><\/h5>\n<\/td>\n<td>\n<h5><a href=\"https:\/\/entri.app\/course\/python-programming-course\/\"><strong>Python Programming Course<\/strong><\/a><\/h5>\n<\/td>\n<td>\n<h5><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\"><strong>Data Science and Machine Learning Course<\/strong><\/a><\/h5>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"Advantages_and_disadvantages\"><\/span><strong>Advantages and disadvantages<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-9a90f8f elementor-widget elementor-widget-text-editor\" data-id=\"9a90f8f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n<div class=\"elementor-widget-container\">\n<p>To start with, let\u2019s talk about the advantages. Random Forest is based on the Bagging technique that helps to promote the algorithm\u2019s performance. Random Forest is no exception. It works well \u201cout-of-the-box\u201d with no hyperparameter tuning and way better than linear algorithms which makes it a good option. Moreover, Random Forest is rather fast, robust, and can show feature importances which can be quite useful.<\/p>\n<p>Also, Random Forest limits the greatest disadvantage of Decision Trees. It almost does not overfit due to subset and feature randomization. Firstly, it uses a unique subset of the initial data for every base model which helps to make Decision Trees less correlated. Secondly, it splits each node in every Decision Tree using a random set of features. Such an approach means that no single tree sees all the data, which helps to focus on the general patterns within the training data,and reduces sensitivity to noise.<\/p>\n<p>Nevertheless, Random Forest has disadvantages. Despite being an improvement over a single Decision Tree, there are more complex techniques than Random Forest. To tell the truth, the best prediction accuracy on difficult problems is usually obtained by Boosting algorithms.<\/p>\n<p>Also, Random Forest is not able to extrapolate based on the data. The predictions it makes are always in the range of the training set. It is a major disadvantage as not every Regression problem can be solved using Random Forest. The Random Forest Regressor is unable to discover trends that would enable it in extrapolating values that fall outside the training set.\u00a0Actually, that is why Random Forest is used mostly for the Classification task.<\/p>\n<p style=\"text-align: center;\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/course\/python-programming-course\/\" target=\"_blank\" rel=\"noopener\">&#8220;Ready to take your python skills to the next level? Sign up for a free demo today!&#8221;<\/a><\/strong><\/p>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-50f5271 elementor-widget elementor-widget-heading\" data-id=\"50f5271\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n<div class=\"elementor-widget-container\">\n<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"When_to_use_Random_Forest_in_real_life\"><\/span><strong>When to use Random Forest in real life<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-b071a55 elementor-widget elementor-widget-text-editor\" data-id=\"b071a55\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n<div class=\"elementor-widget-container\">\n<p>As mentioned above, Random Forest is used mostly to solve Classification problems. It is worth noting that Random Forest is rarely used in production simply because of other algorithms showing better performance. However, RF is a must-have algorithm for hypothesis testing as it may help you to get valuable insights. For example, the \u201cout-of-the-box\u201d Random Forest model was good enough to show a better performance on a difficult\u00a0Fraud Detection\u00a0task than a complex multi-model neural network.<\/p>\n<p>From my experience, you might want to try Random Forest as your ML Classification algorithm to solve such problems as:<\/p>\n<ol>\n<li>Fraud Detection (Classification) \u2013 please refer to the article I linked above. You may find it pretty thrilling as it shows how simple ML models can beat complex neural networks on an unobvious task<\/li>\n<li>Credit Scoring (Classification) \u2013 an important solution in the banking sector. Some banks build enormous neural networks to improve this task. However, simple approaches might give the same result<\/li>\n<li>E-commerce case (Classification) \u2013 for example, we can try to predict if the customer will like the product or not<\/li>\n<li>Any Classification problem with a table data, for example, Kaggle competitions<\/li>\n<\/ol>\n<p>In the Regression case, you should use Random Forest if:<\/p>\n<ol>\n<li>It is not a time series problem<\/li>\n<li>The data has a non-linear trend and extrapolation is not crucial<\/li>\n<\/ol>\n<p>For example, Random Forest is frequently used in value prediction (value of a house or a packet of milk from a new brand).<\/p>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-6f045f7 elementor-widget elementor-widget-heading\" data-id=\"6f045f7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n<div class=\"elementor-widget-container\">\n<h4 style=\"text-align: center;\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/course\/python-programming-course\/\" target=\"_blank\" rel=\"noopener\">&#8220;Get hands-on with our python course &#8211; sign up for a free demo!&#8221;<\/a><\/strong><\/h4>\n<h3 class=\"elementor-heading-title elementor-size-default\"><strong>How to use Random Forest for Regression<\/strong><\/h3>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-382b581 elementor-widget elementor-widget-text-editor\" data-id=\"382b581\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n<div class=\"elementor-widget-container\">\n<h4>Setting up<\/h4>\n<p>As mentioned above it is quite easy to use Random Forest. Fortunately, the sklearn library has the algorithm implemented both for the Regression and Classification task. You must use RandomForestRegressor() model for the Regression problem and RandomForestClassifier() for the Classification task.<\/p>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-dbcc542 elementor-widget elementor-widget-heading\" data-id=\"dbcc542\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n<div class=\"elementor-widget-container\">\n<h4 class=\"elementor-heading-title elementor-size-default\">Training<\/h4>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-f8e05f2 elementor-widget elementor-widget-text-editor\" data-id=\"f8e05f2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n<div class=\"elementor-widget-container\">\n<p>If you have ever trained a ML model using sklearn you will have no difficulties working with the RandomForestRegressor. All you need to do is to perform the fit method on your training set and the predict method on the test set.<\/p>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-cbbd322 elementor-widget elementor-widget-elementor-syntax-highlighter\" data-id=\"cbbd322\" data-element_type=\"widget\" data-widget_type=\"elementor-syntax-highlighter.default\">\n<div class=\"elementor-widget-container\">\n<pre class=\" language-python\"><code class=\" language-python\">random_forest<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span>\r\ny_pred <span class=\"token operator\">=<\/span> random_forest<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>X_test<span class=\"token punctuation\">)<\/span> <\/code><\/pre>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-e77924d elementor-widget elementor-widget-text-editor\" data-id=\"e77924d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n<div class=\"elementor-widget-container\">\n<p>However, Random Forest in sklearn does not automatically handle the missing values. The algorithm will return an error if it finds any NaN or Null values in your data. If you want to check it for yourself please refer to the \u201cMissing values\u201d section of the notebook. Of course, you may easily drop all the samples with the missing values and continue training. Still, there are some non-standard\u00a0techniques that will help you overcome this problem.<\/p>\n<p>Overall, please do not forget about the EDA. It is always better to study your data, normalize it, handle the categorical features and the missing values before you even start training. That way you will be able to avoid many obstacles.<\/p>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-8cb3fb7 elementor-widget elementor-widget-heading\" data-id=\"8cb3fb7\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n<div class=\"elementor-widget-container\">\n<h4 class=\"elementor-heading-title elementor-size-default\">Tuning<\/h4>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-8096b05 elementor-widget elementor-widget-text-editor\" data-id=\"8096b05\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n<div class=\"elementor-widget-container\">\n<p>In general, you should always tune your model as it must help to enhance the algorithm\u2019s performance. As you might know, tuning is a really expensive process time-wise. When tuning a Random Forest model it gets even worse as you must train hundreds of trees multiple times for each parameter grid subset. So, you must not be afraid. Trust me, it is worth it.<\/p>\n<p>You can easily tune a RandomForestRegressor model using GridSearchCV. If you are not sure what model hyperparameters you want to add to your parameter grid, please refer either to the sklearn\u00a0official documentation\u00a0or the\u00a0Kaggle notebooks. Sklearn documentation will help you find out what hyperparameters the RandomForestRegressor has. Kaggle notebooks, on the other hand, will feature parameter grids of other users which may be quite helpful.<\/p>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-b87e864 elementor-widget elementor-widget-elementor-syntax-highlighter\" data-id=\"b87e864\" data-element_type=\"widget\" data-widget_type=\"elementor-syntax-highlighter.default\">\n<div class=\"elementor-widget-container\">\n<pre class=\" language-python\"><code class=\" language-python\">andom_forest_tuning <span class=\"token operator\">=<\/span> RandomForestRegressor<span class=\"token punctuation\">(<\/span>random_state <span class=\"token operator\">=<\/span> SEED<span class=\"token punctuation\">)<\/span>\r\nparam_grid <span class=\"token operator\">=<\/span> <span class=\"token punctuation\">{<\/span>\r\n   <span class=\"token string\">'n_estimators'<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">100<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">200<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token number\">500<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span>\r\n   <span class=\"token string\">'max_features'<\/span><span class=\"token punctuation\">:<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token string\">'auto'<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">'sqrt'<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">'log2'<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span>\r\n   <span class=\"token string\">'max_depth'<\/span> <span class=\"token punctuation\">:<\/span> <span class=\"token punctuation\">[<\/span><span class=\"token number\">4<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">5<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">6<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">7<\/span><span class=\"token punctuation\">,<\/span><span class=\"token number\">8<\/span><span class=\"token punctuation\">]<\/span><span class=\"token punctuation\">,<\/span>\r\n   <span class=\"token string\">'criterion'<\/span> <span class=\"token punctuation\">:<\/span><span class=\"token punctuation\">[<\/span><span class=\"token string\">'mse'<\/span><span class=\"token punctuation\">,<\/span> <span class=\"token string\">'mae'<\/span><span class=\"token punctuation\">]<\/span>\r\n<span class=\"token punctuation\">}<\/span>\r\nGSCV <span class=\"token operator\">=<\/span> GridSearchCV<span class=\"token punctuation\">(<\/span>estimator<span class=\"token operator\">=<\/span>random_forest_tuning<span class=\"token punctuation\">,<\/span> param_grid<span class=\"token operator\">=<\/span>param_grid<span class=\"token punctuation\">,<\/span> cv<span class=\"token operator\">=<\/span><span class=\"token number\">5<\/span><span class=\"token punctuation\">)<\/span>\r\nGSCV<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span>\r\nGSCV<span class=\"token punctuation\">.<\/span>best_params_ <\/code><\/pre>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-55dedba elementor-widget elementor-widget-heading\" data-id=\"55dedba\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n<div class=\"elementor-widget-container\">\n<h4 class=\"elementor-heading-title elementor-size-default\">Testing<\/h4>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-108c508 elementor-widget elementor-widget-text-editor\" data-id=\"108c508\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n<div class=\"elementor-widget-container\">\n<p>When you have your model trained and tuned, it is time to test its final performance. Random Forest is just another Regression algorithm, so you can use all the regression metrics to assess its result.<\/p>\n<p>For example, you might use MAE, MSE, MASE, RMSE, MAPE, SMAPE, and\u00a0others. However, from my experience,\u00a0MAE\u00a0and\u00a0MSE\u00a0are the most commonly used. Both of them will be a good fit to evaluate the model\u2019s performance. So, if you use them, keep in mind that the less is your error, the better and the error of the perfect model will be equal to zero.<\/p>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-224dc4d elementor-widget elementor-widget-elementor-syntax-highlighter\" data-id=\"224dc4d\" data-element_type=\"widget\" data-widget_type=\"elementor-syntax-highlighter.default\">\n<div class=\"elementor-widget-container\">\n<pre class=\" language-python\"><code class=\" language-python\">random_forest <span class=\"token operator\">=<\/span> RandomForestRegressor<span class=\"token punctuation\">(<\/span>random_state <span class=\"token operator\">=<\/span> SEED<span class=\"token punctuation\">)<\/span>\r\nrandom_forest<span class=\"token punctuation\">.<\/span>fit<span class=\"token punctuation\">(<\/span>X_train<span class=\"token punctuation\">,<\/span> y_train<span class=\"token punctuation\">)<\/span>\r\ny_pred <span class=\"token operator\">=<\/span> random_forest<span class=\"token punctuation\">.<\/span>predict<span class=\"token punctuation\">(<\/span>X_test<span class=\"token punctuation\">)<\/span>\r\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'MAE: '<\/span><span class=\"token punctuation\">,<\/span> mean_absolute_error<span class=\"token punctuation\">(<\/span>y_test<span class=\"token punctuation\">,<\/span> y_pred<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span>\r\n<span class=\"token keyword\">print<\/span><span class=\"token punctuation\">(<\/span><span class=\"token string\">'MSE: '<\/span><span class=\"token punctuation\">,<\/span> mean_squared_error<span class=\"token punctuation\">(<\/span>y_test<span class=\"token punctuation\">,<\/span> y_pred<span class=\"token punctuation\">)<\/span><span class=\"token punctuation\">)<\/span> <\/code><\/pre>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-30349e2 elementor-widget elementor-widget-text-editor\" data-id=\"30349e2\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n<div class=\"elementor-widget-container\">\n<p>Also, it is worth mentioning that you might not want to use any Cross-Validation technique to check the model\u2019s ability to generalize. Some Data Scientists think that the Random Forest algorithm provides free Cross-Validation.\u00a0You see, Random Forest randomizes the feature selection during each tree split, so that it does not overfit like other models. That is why using Cross-Validation on the Random Forest model might be unnecessary.<\/p>\n<p>Still, if you want to use the Cross-Validation technique you can use the hold-out set concept. As mentioned before, samples from the original dataset that did not appear in any subset are called \u201cout-of-bag\u201d samples. They are a perfect fit for the hold-out set. Generally, using \u201cout-of-bag\u201d samples as a hold-out set will be enough for you to understand if your model generalizes well.<\/p>\n<h4 style=\"text-align: center;\"><span data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;\\&quot;Experience the power of our web development course with a free demo - enroll now!\\&quot;&quot;}\" data-sheets-userformat=\"{&quot;2&quot;:1061379,&quot;3&quot;:{&quot;1&quot;:0},&quot;4&quot;:{&quot;1&quot;:2,&quot;2&quot;:16777215},&quot;12&quot;:0,&quot;15&quot;:&quot;Roboto, RobotoDraft, Helvetica, Arial, sans-serif&quot;,&quot;16&quot;:10,&quot;23&quot;:1}\" data-sheets-hyperlink=\"https:\/\/entri.app\/course\/python-programming-course\/\"><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/course\/python-programming-course\/\" target=\"_blank\" rel=\"noopener\">&#8220;Experience the power of our web development course with a free demo &#8211; enroll now!&#8221;<\/a><\/span><\/h4>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-c4af187 elementor-widget elementor-widget-heading\" data-id=\"c4af187\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n<div class=\"elementor-widget-container\">\n<h2 class=\"elementor-heading-title elementor-size-default\"><span class=\"ez-toc-section\" id=\"Final_Thoughts\"><\/span><strong>Final Thoughts<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<\/div>\n<\/div>\n<div class=\"elementor-element elementor-element-cd9ce9f elementor-widget elementor-widget-text-editor\" data-id=\"cd9ce9f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n<div class=\"elementor-widget-container\">\n<p>To summarize, we started with some theoretical information about Ensemble Learning, ensemble types, Bagging and Random Forest algorithms and went through a step-by-step guide on how to use Random Forest in Python for the Regression task. Also, we compared Random Forest with some other ML Regression algorithms. Lastly, we talked about some tips you may find useful when working with Random Forest.<\/p>\n<p><a href=\"https:\/\/entri.app\/course\/python-programming-course\/\" target=\"_blank\" rel=\"noopener\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-25522670 size-full\" src=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Rectangle-1.png\" alt=\"Python and Machine Learning Rectangle\" width=\"970\" height=\"250\" srcset=\"https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Rectangle-1.png 970w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Rectangle-1-300x77.png 300w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Rectangle-1-768x198.png 768w, https:\/\/entri.app\/blog\/wp-content\/uploads\/2022\/04\/Python-and-Machine-Learning-Rectangle-1-750x193.png 750w\" sizes=\"auto, (max-width: 970px) 100vw, 970px\" \/><\/a><\/p>\n<h4><strong>Related Articles<\/strong><\/h4>\n<div class=\"table-responsive wprt_style_display\">\n<table class=\"table\" dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\">\n<colgroup>\n<col width=\"329\" \/>\n<col width=\"309\" \/><\/colgroup>\n<tbody>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Syllabus&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-village-field-assistant-vfa-syllabus-exam-pattern\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/how-to-become-a-data-scientist-in-india\/\" target=\"_blank\" rel=\"noopener\">How to Become a Data Scientist in India?<\/a><\/strong><\/td>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Mock Test&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-vfa-free-mock-test\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/data-analysis-process-methods-types\/\" target=\"_blank\" rel=\"noopener\">Data Analysis &#8211; Process, Methods, Types<\/a><\/strong><\/td>\n<\/tr>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Exam Date&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-vfa-exam-date\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/how-to-learn-python-at-home\/\" target=\"_blank\" rel=\"noopener\">How To Learn Python At Home?<\/a><\/strong><\/td>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Video Course&quot;}\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/top-data-types-of-python-python-data-types\/\" target=\"_blank\" rel=\"noopener\">Top Data Types of Python<\/a><\/strong><\/td>\n<\/tr>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Application Form&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-vfa-apply-online\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/data-science-jobs-in-kerala\/\" target=\"_blank\" rel=\"noopener\">Data Science Jobs in Kerala<\/a><\/strong><\/td>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Study Materials&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-vfa-study-material\/\"><a href=\"https:\/\/entri.app\/blog\/switch-case-in-python-switch-function\/\"><strong>Switch Case in Python<\/strong><\/a><\/td>\n<\/tr>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Vacancy&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-vfa-vacancy\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/top-10-applications-of-machine-learning-in-2022\/\" target=\"_blank\" rel=\"noopener\">Top 10 Applications of Machine Learning in 2023<\/a><\/strong><\/td>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Interview Questions&quot;}\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/best-full-stack-developer-course-with-placement\/\" target=\"_blank\" rel=\"noopener\">Best Full Stack Developer Course with Placement<\/a><\/strong><\/td>\n<\/tr>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Admit Card&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-vfa-admit-card\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/future-of-python-developers-in-2022\/\" target=\"_blank\" rel=\"noopener\">Future of Python Developers<\/a><\/strong><\/td>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Kerala PSC VFA Job Profile&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/kerala-psc-vfa-job-profile\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/what-is-data-interpretation-methods-and-benefits\/\" target=\"_blank\" rel=\"noopener\">What is Data Interpretation? Methods and Benefits<\/a><\/strong><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<div class=\"modal\" id=\"modal25556851\"><div class=\"modal-content\"><span class=\"close-button\">&times;<\/span>\n\n<div class=\"wpcf7 no-js\" id=\"wpcf7-f25556851-o1\" lang=\"en-US\" dir=\"ltr\" data-wpcf7-id=\"25556851\">\n<div class=\"screen-reader-response\"><p role=\"status\" aria-live=\"polite\" aria-atomic=\"true\"><\/p> <ul><\/ul><\/div>\n<form action=\"\/blog\/wp-json\/wp\/v2\/posts\/25545913#wpcf7-f25556851-o1\" method=\"post\" class=\"wpcf7-form init\" aria-label=\"Contact form\" novalidate=\"novalidate\" data-status=\"init\">\n<fieldset class=\"hidden-fields-container\"><input type=\"hidden\" name=\"_wpcf7\" value=\"25556851\" \/><input type=\"hidden\" name=\"_wpcf7_version\" value=\"6.1.4\" \/><input type=\"hidden\" name=\"_wpcf7_locale\" value=\"en_US\" \/><input type=\"hidden\" name=\"_wpcf7_unit_tag\" value=\"wpcf7-f25556851-o1\" \/><input type=\"hidden\" name=\"_wpcf7_container_post\" value=\"0\" \/><input type=\"hidden\" name=\"_wpcf7_posted_data_hash\" value=\"\" \/><input type=\"hidden\" name=\"_wpcf7cf_hidden_group_fields\" value=\"[]\" \/><input type=\"hidden\" name=\"_wpcf7cf_hidden_groups\" value=\"[]\" \/><input type=\"hidden\" name=\"_wpcf7cf_visible_groups\" value=\"[]\" \/><input type=\"hidden\" name=\"_wpcf7cf_repeaters\" value=\"[]\" \/><input type=\"hidden\" name=\"_wpcf7cf_steps\" value=\"{}\" \/><input type=\"hidden\" name=\"_wpcf7cf_options\" value=\"{&quot;form_id&quot;:25556851,&quot;conditions&quot;:[],&quot;settings&quot;:{&quot;animation&quot;:&quot;yes&quot;,&quot;animation_intime&quot;:200,&quot;animation_outtime&quot;:200,&quot;conditions_ui&quot;:&quot;normal&quot;,&quot;notice_dismissed&quot;:false,&quot;notice_dismissed_update-cf7-5.9.8&quot;:true,&quot;notice_dismissed_update-cf7-6.1.1&quot;:true}}\" \/>\n<\/fieldset>\n<p><span class=\"wpcf7-form-control-wrap\" data-name=\"full_name\"><input size=\"40\" maxlength=\"400\" class=\"wpcf7-form-control wpcf7-text wpcf7-validates-as-required\" aria-required=\"true\" aria-invalid=\"false\" placeholder=\"Name\" value=\"\" type=\"text\" name=\"full_name\" \/><\/span><br \/>\n<span class=\"wpcf7-form-control-wrap\" data-name=\"phone\"><input size=\"40\" maxlength=\"400\" class=\"wpcf7-form-control wpcf7-tel wpcf7-validates-as-required wpcf7-text wpcf7-validates-as-tel\" aria-required=\"true\" aria-invalid=\"false\" placeholder=\"Phone\" value=\"\" type=\"tel\" name=\"phone\" \/><\/span><br \/>\n<span class=\"wpcf7-form-control-wrap\" data-name=\"email_id\"><input size=\"40\" maxlength=\"400\" class=\"wpcf7-form-control wpcf7-email wpcf7-text wpcf7-validates-as-email\" aria-invalid=\"false\" placeholder=\"Email\" value=\"\" type=\"email\" name=\"email_id\" \/><\/span>\n<\/p>\n<div class=\"custom-form-group-1\">\n\t<p><span class=\"wpcf7-form-control-wrap\" data-name=\"language\"><select class=\"wpcf7-form-control wpcf7-select wpcf7-validates-as-required language-select1\" aria-required=\"true\" aria-invalid=\"false\" name=\"language\"><option value=\"\">Select Language<\/option><option value=\"Malayalam\">Malayalam<\/option><option value=\"Tamil\">Tamil<\/option><option value=\"Telugu\">Telugu<\/option><option value=\"Kannada\">Kannada<\/option><\/select><\/span>\n\t<\/p>\n<\/div>\n<div class=\"custom-form-group-1\">\n\t<p><span class=\"wpcf7-form-control-wrap\" data-name=\"course\"><select class=\"wpcf7-form-control wpcf7-select wpcf7-validates-as-required course-select1\" aria-required=\"true\" aria-invalid=\"false\" name=\"course\"><option value=\"\">Select an option<\/option><option value=\"Kerala PSC Exams\">Kerala PSC Exams<\/option><option value=\"Kerala PSC Teaching Exams\">Kerala PSC Teaching Exams<\/option><option value=\"Kerala PSC Technical Exams\">Kerala PSC Technical Exams<\/option><option value=\"SSC\/RRB\">SSC\/RRB<\/option><option value=\"GATE\">GATE<\/option><option value=\"Banking &amp; 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In the case of a classification [&hellip;]<\/p>\n","protected":false},"author":111,"featured_media":25546256,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[802,1903,1904,1841,1888,1881],"tags":[],"class_list":["post-25545913","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles","category-coding","category-entri-elevate","category-entri-skilling","category-python-programming","category-web-android-development"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Random Forest Regression in Python - Entri Blog<\/title>\n<meta name=\"description\" content=\"Random Forest is an ensemble technique capable of performing both regression and classification tasks with the use of multiple decision trees and a technique.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/entri.app\/blog\/random-forest-regression-in-python\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Random Forest Regression in Python - 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