{"id":5512,"date":"2023-12-13T04:54:41","date_gmt":"2023-12-13T04:54:41","guid":{"rendered":"https:\/\/www.pickl.ai\/blog\/?p=5512"},"modified":"2024-07-17T09:43:52","modified_gmt":"2024-07-17T09:43:52","slug":"regularization-in-machine-learning","status":"publish","type":"post","link":"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/","title":{"rendered":"Regularisation in Machine Learning: All you need to know"},"content":{"rendered":"<p><b>Summary:<\/b><span style=\"font-weight: 400;\"> Regularisation methods like L1 (Lasso) and L2 (Ridge) curb overfitting by penalising model complexity. Elastic Net blends both approaches, while Dropout enhances neural networks by diversifying feature learning. These techniques ensure models perform well on new data, which is critical for robust Machine Learning applications.<\/span><\/p>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_81 counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><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><\/span><\/a><\/span><\/div>\n<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:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Introduction\" >Introduction<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#What_is_Regularisation_in_Machine_Learning\" >What is Regularisation in Machine Learning?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#What_is_Overfitting_in_Machine_Learning\" >What is Overfitting in Machine Learning?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#What_is_Underfitting_in_Machine_Learning\" >What is Underfitting in Machine Learning?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Types_of_Regularisation\" >Types of Regularisation<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#L1_Regularisation_Lasso\" >L1 Regularisation (Lasso)<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#How_L1_Regularisation_Works\" >How L1 Regularisation Works<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Advantages_of_L1_Regularisation\" >Advantages of L1 Regularisation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Applications_of_L1_Regularisation\" >Applications of L1 Regularisation<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#L2_Regularisation_Ridge\" >L2 Regularisation (Ridge)<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#How_L2_Regularisation_Works\" >How L2 Regularisation Works<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Advantages_of_L2_Regularisation\" >Advantages of L2 Regularisation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Applications_of_L2_Regularisation\" >Applications of L2 Regularisation<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Elastic_Net_Regularisation\" >Elastic Net Regularisation<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#How_Elastic_Net_Regularisation_Works\" >How Elastic Net Regularisation Works<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-16\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Advantages_of_Elastic_Net_Regularisation\" >Advantages of Elastic Net Regularisation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-17\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Applications_of_Elastic_Net_Regularisation\" >Applications of Elastic Net Regularisation<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-18\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Dropout_Regularisation\" >Dropout Regularisation<\/a><ul class='ez-toc-list-level-4' ><li class='ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-19\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#How_Dropout_Regularisation_Works\" >How Dropout Regularisation Works<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-20\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Advantages_of_Dropout_Regularisation\" >Advantages of Dropout Regularisation<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-4'><a class=\"ez-toc-link ez-toc-heading-21\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Applications_of_Dropout_Regularisation\" >Applications of Dropout Regularisation<\/a><\/li><\/ul><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-22\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Choosing_the_Right_Regularisation_Technique\" >Choosing the Right Regularisation Technique<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-23\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Frequently_Asked_Questions\" >Frequently Asked Questions<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-24\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#What_is_regularisation_in_Machine_Learning\" >What is regularisation in Machine Learning?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-25\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Which_regularisation_technique_is_best_for_feature_selection\" >Which regularisation technique is best for feature selection?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-26\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#How_does_dropout_regularisation_improve_neural_networks\" >How does dropout regularisation improve neural networks?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-27\" href=\"https:\/\/www.pickl.ai\/blog\/regularization-in-machine-learning\/#Conclusion\" >Conclusion<\/a><\/li><\/ul><\/nav><\/div>\n<h2 id=\"introduction\"><span class=\"ez-toc-section\" id=\"Introduction\"><\/span><b>Introduction<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Preventing overfitting or underfitting during model training is crucial in <\/span><a href=\"https:\/\/pickl.ai\/blog\/what-is-machine-learning\/\"><span style=\"font-weight: 400;\">Machine Learning<\/span><\/a><span style=\"font-weight: 400;\">. Hence, regularisation becomes pivotal. These techniques are vital in achieving this balance and creating an optimal model.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In this article, we will explore different types of regularisation in Machine Learning and how they help overcome <\/span><a href=\"https:\/\/pickl.ai\/blog\/difference-between-underfitting-and-overfitting\/\"><span style=\"font-weight: 400;\">overfitting and underfitting<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"what-is-regularisation-in-machine-learning\"><span class=\"ez-toc-section\" id=\"What_is_Regularisation_in_Machine_Learning\"><\/span><b>What is Regularisation in Machine Learning?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Regularisation in Machine Learning is a technique used to enhance model performance by preventing overfitting. Overfitting occurs when a model learns the noise in the training data instead of the actual patterns, leading to poor performance on unseen data. Regularisation adds a penalty to the model&#8217;s complexity, discouraging it from fitting the noise.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Regularisation techniques like <\/span><a href=\"https:\/\/pickl.ai\/blog\/l1-and-l2-regularization-in-machine-learning\/\"><span style=\"font-weight: 400;\">L1 and L2<\/span><\/a><span style=\"font-weight: 400;\"> are vital in achieving a balanced and efficient Machine Learning model.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">It helps create robust models that generalise new data well. Controlling the model&#8217;s complexity strikes a balance between underfitting and overfitting, leading to better predictive performance.<\/span><\/p>\n<h2 id=\"what-is-overfitting-in-machine-learning\"><span class=\"ez-toc-section\" id=\"What_is_Overfitting_in_Machine_Learning\"><\/span><b>What is Overfitting in Machine Learning?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Overfitting in Machine Learning happens when a model learns not only the underlying pattern of the training data but also its noise and outliers. This results in a model that performs exceptionally well on training data but poorly on new, unseen data.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Overfitting can occur due to an overly complex model with too many parameters relative to the number of observations. Techniques such as cross-validation, pruning, or regularisation can be employed to combat overfitting. Ensuring a balance between model complexity and generalisation ability is crucial for achieving robust performance on new datasets.<\/span><\/p>\n<h2 id=\"what-is-underfitting-in-machine-learning\"><span class=\"ez-toc-section\" id=\"What_is_Underfitting_in_Machine_Learning\"><\/span><b>What is <\/b><b>Underfitting<\/b><b> in Machine Learning?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Underfitting in Machine Learning occurs when a model fails to capture the underlying patterns in the data. This happens because the model is too simple, unable to accurately represent the complexity of the data.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">As a result, it performs poorly on both the training data and unseen data, leading to high bias and low variance. Common causes of underfitting include using an insufficient number of features, an overly simplistic algorithm, or inadequate training time.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">To mitigate underfitting, one can increase model complexity, add more relevant features, or adjust hyperparameters to fit the data better.<\/span><\/p>\n<p><b>Read Blog:<\/b> <a href=\"https:\/\/pickl.ai\/blog\/understanding-radial-basis-function-in-machine-learning\/\"><span style=\"font-weight: 400;\">Understanding Radial Basis Function In Machine Learning<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"types-of-regularisation\"><span class=\"ez-toc-section\" id=\"Types_of_Regularisation\"><\/span><b>Types of Regularisation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img fetchpriority=\"high\" decoding=\"async\" class=\"alignnone size-full wp-image-11877\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image1-4.jpg\" alt=\"Types of Regularisation\" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image1-4.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image1-4-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image1-4-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image1-4-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image1-4-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image1-4-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image1-4-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image1-4-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image1-4-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image1-4-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Several regularisation methods exist, each with its unique approach and benefits. Here, we will explore the most commonly used regularisation techniques: L1 regularisation (Lasso), L2 regularisation (Ridge), Elastic Net, and Dropout.<\/span><\/p>\n<h3 id=\"l1-regularisation-lasso\"><span class=\"ez-toc-section\" id=\"L1_Regularisation_Lasso\"><\/span><b>L1 Regularisation (Lasso)<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/pickl.ai\/blog\/lasso-regression\/\"><span style=\"font-weight: 400;\">L1 regularisation<\/span><\/a><span style=\"font-weight: 400;\">, also known as Lasso (Least Absolute Shrinkage and Selection Operator), adds a penalty equal to the absolute value of the coefficients. This form of regularisation sets some of the coefficients to zero, effectively performing feature selection and leading to sparse models.<\/span><\/p>\n<h4 id=\"how-l1-regularisation-works\"><span class=\"ez-toc-section\" id=\"How_L1_Regularisation_Works\"><\/span><b>How L1 Regularisation Works<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">The L1 regularisation term is added to the loss function, which looks like this:<\/span><\/p>\n<p><img decoding=\"async\" class=\"alignnone size-full wp-image-11883\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image4.png\" alt=\"\" width=\"390\" height=\"48\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image4.png 390w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image4-300x37.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image4-110x14.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image4-200x25.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image4-380x47.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image4-255x31.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image4-150x18.png 150w\" sizes=\"(max-width: 390px) 100vw, 390px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">In this equation, the regularisation term <img decoding=\"async\" class=\"alignnone size-full wp-image-11884\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image7.png\" alt=\"\" width=\"116\" height=\"31\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image7.png 116w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image7-110x29.png 110w\" sizes=\"(max-width: 116px) 100vw, 116px\" \/>\u00a0<\/span><span style=\"font-weight: 400;\"> is added to the original loss function. Here, <img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-11885\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image8.png\" alt=\"\" width=\"32\" height=\"27\" \/> <\/span><span style=\"font-weight: 400;\">represents the model&#8217;s coefficients, and n is the number of coefficients. The parameter \u03bb, known as the regularisation parameter, controls the strength of this penalty.<\/span><\/p>\n<p><b>The role of \u03bb is crucial:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Higher \u03bb values<\/b><span style=\"font-weight: 400;\">: When \u03bb is large, the penalty increases, which results in more coefficients being shrunk towards zero. This effectively reduces the number of non-zero coefficients, promoting sparsity in the model. In other words, many coefficients will be precisely zero, simplifying the model by retaining only the most significant features.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Lower \u03bb values<\/b><span style=\"font-weight: 400;\">: Conversely, a smaller \u03bb value results in a weaker penalty, allowing more coefficients to remain significant.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">By carefully tuning \u03bb, L1 regularisation can balance model complexity and performance, preventing overfitting and enhancing interpretability through feature selection.<\/span><\/p>\n<h4 id=\"advantages-of-l1-regularisation\"><span class=\"ez-toc-section\" id=\"Advantages_of_L1_Regularisation\"><\/span><b>Advantages of L1 Regularisation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">L1 regularisation offers several advantages, making it a valuable tool in Machine Learning. By adding a penalty equal to the absolute value of the coefficients, L1 regularisation prevents overfitting, simplifies the model, and addresses multicollinearity. Here are the key benefits:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Feature Selection:<\/b><span style=\"font-weight: 400;\"> Lasso drives some coefficients to zero, effectively performing feature selection. This results in a simpler model that includes only the most relevant features, enhancing interpretability and reducing the risk of overfitting.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Handling Multicollinearity: <\/b><span style=\"font-weight: 400;\">In high-dimensional datasets with correlated features, Lasso helps by selecting a subset of these features, mitigating the impact of multicollinearity and improving model stability.<\/span><\/li>\n<\/ul>\n<h4 id=\"applications-of-l1-regularisation\"><span class=\"ez-toc-section\" id=\"Applications_of_L1_Regularisation\"><\/span><b>Applications of L1 Regularisation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">L1 regularisation is widely used in fields such as genetics, where the number of predictors (genes) can be much larger than the number of observations. Researchers often deal with thousands of genes in genetic studies, but only a few may be relevant to a particular disease or trait.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">L1 regularisation helps identify these key genes by shrinking the coefficients of less important ones to zero, effectively performing feature selection. This simplifies the model and makes it more interpretable, enabling researchers to focus on the most predictive genes and gain deeper insights into genetic associations and biological mechanisms.<\/span><\/p>\n<h3 id=\"l2-regularisation-ridge\"><span class=\"ez-toc-section\" id=\"L2_Regularisation_Ridge\"><\/span><b>L2 Regularisation (Ridge)<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">L2 regularisation, also known as Ridge regression, adds a penalty equal to the square of the magnitude of coefficients. Unlike L1 regularisation, Ridge does not set any coefficients to zero but shrinks them towards zero, ensuring all features contribute to the prediction.<\/span><\/p>\n<h4 id=\"how-l2-regularisation-works\"><span class=\"ez-toc-section\" id=\"How_L2_Regularisation_Works\"><\/span><b>How L2 Regularisation Works<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">The modified loss function with L2 regularisation looks like this:<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-11887\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image3.png\" alt=\"\" width=\"409\" height=\"57\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image3.png 409w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image3-300x42.png 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image3-110x15.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image3-200x28.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image3-380x53.png 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image3-255x36.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image3-150x21.png 150w\" sizes=\"(max-width: 409px) 100vw, 409px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">In this equation, \u03bb (lambda) is the regularisation parameter that controls the strength of the penalty. The term <\/span><span style=\"font-weight: 400;\"> represents <img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-11888\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image6.png\" alt=\"\" width=\"94\" height=\"36\" \/> the sum of the squares of all the model&#8217;s coefficients. By adding this term to the loss function, L2 regularisation discourages the model from learning overly complex patterns that might fit the training data too closely.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">A larger \u03bb value increases the penalty, leading to greater coefficient shrinkage. This results in smaller coefficient values, which helps to reduce the model&#8217;s variance. In other words, by controlling the magnitude of the coefficients, L2 regularisation ensures that the model generalises better to new, unseen data.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">L2 regularisation is an effective technique for improving the performance and robustness of <\/span><a href=\"https:\/\/pickl.ai\/blog\/how-to-build-a-machine-learning-model\/\"><span style=\"font-weight: 400;\">Machine Learning models<\/span><\/a><span style=\"font-weight: 400;\">. It balances the trade-off between fitting the training data well and keeping the model&#8217;s complexity in check.<\/span><\/p>\n<h4 id=\"advantages-of-l2-regularisation\"><span class=\"ez-toc-section\" id=\"Advantages_of_L2_Regularisation\"><\/span><b>Advantages of L2 Regularisation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">L2 regularisation is a powerful technique for improving the performance and robustness of Machine Learning models. It offers several key advantages that enhance the model&#8217;s ability to generalise to new data.<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Preventing Overfitting<\/b><span style=\"font-weight: 400;\">: L2 regularisation helps prevent overfitting by incorporating a penalty for large coefficients. This is particularly useful when the number of features is large, as it ensures that the model does not become too complex and fits the noise in the training data.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Handling Multicollinearity<\/b><span style=\"font-weight: 400;\">: Ridge regression effectively addresses multicollinearity by shrinking correlated features together. This results in more stable and reliable estimates of the coefficients, which can improve the model&#8217;s predictive accuracy and interpretability.<\/span><\/li>\n<\/ul>\n<h4 id=\"applications-of-l2-regularisation\"><span class=\"ez-toc-section\" id=\"Applications_of_L2_Regularisation\"><\/span><b>Applications of L2 Regularisation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">L2 regularisation is commonly use in scenarios with many predictors, such as text classification and image recognition. In text classification, it helps manage the vast number of features derived from text data, reducing overfitting and improving model generalisation.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In image recognition, L2 regularisation ensures that the model learns from the essential features of the images without being overly influenced by noise or irrelevant details. By penalising large coefficients, L2 regularisation creates robust models that generalise to new, unseen data, enhancing their predictive performance in various complex tasks.<\/span><\/p>\n<h3 id=\"elastic-net-regularisation\"><span class=\"ez-toc-section\" id=\"Elastic_Net_Regularisation\"><\/span><b>Elastic Net Regularisation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/en.wikipedia.org\/wiki\/Elastic_net_regularization\"><span style=\"font-weight: 400;\">Elastic Net regularisation <\/span><\/a><span style=\"font-weight: 400;\">effectively blends the feature selection capabilities of L1 (Lasso) and the coefficient shrinkage of L2 (Ridge) regularisation. This dual-penalty approach is ideal for scenarios with numerous correlated predictors, allowing it to handle multicollinearity by grouping and selecting relevant sets of features.<\/span><\/p>\n<h4 id=\"how-elastic-net-regularisation-works\"><span class=\"ez-toc-section\" id=\"How_Elastic_Net_Regularisation_Works\"><\/span><b>How Elastic Net Regularisation Works<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">The loss function of Elastic Net is formulated by adding two components to the base loss: <\/span><span style=\"font-weight: 400;\">\u200b.<\/span><\/p>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-11890\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image5.png\" alt=\"\" width=\"288\" height=\"43\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image5.png 288w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image5-110x16.png 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image5-200x30.png 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image5-255x38.png 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image5-150x22.png 150w\" sizes=\"(max-width: 288px) 100vw, 288px\" \/> <span style=\"font-size: revert;\">Here, \u03bb1 and \u03bb2\u200b are regularisation parameters that govern the strength of the L1 and L2 penalties, respectively.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The L1 penalty encourages sparsity in the model by shrinking coefficients towards zero, facilitating feature selection. On the other hand, the L2 penalty ensures all features contribute by penalising large coefficients and handling multicollinearity.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">By adjusting \u03bb1 and \u03bb2\u200b, practitioners can fine-tune the balance between feature selection and model simplicity. Elastic Net regularisation is particularly effective in scenarios where datasets are high-dimensional and contain correlated features.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">This methodological flexibility and ability to address diverse data challenges make Elastic Net a widely used regularisation technique in Machine Learning and statistical modelling.<\/span><\/p>\n<h4 id=\"advantages-of-elastic-net-regularisation\"><span class=\"ez-toc-section\" id=\"Advantages_of_Elastic_Net_Regularisation\"><\/span><b>Advantages of Elastic Net Regularisation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Elastic Net regularisation blends the advantages of Lasso and Ridge techniques, offering a versatile approach to model regularisation in Machine Learning. Integrating L1 and L2 penalties effectively manages feature selection and simultaneously addresses multicollinearity challenges. This dual regularisation method provides several key benefits:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Feature Selection<\/b><span style=\"font-weight: 400;\">: Enables the identification of relevant predictors by driving less influential coefficients to zero.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Multicollinearity Handling<\/b><span style=\"font-weight: 400;\">: Mitigates the impact of correlated predictors, ensuring robust model performance.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Flexibility in Tuning<\/b><span style=\"font-weight: 400;\">: Allows fine-grained control over the balance between L1 and L2 penalties through \u03bb1 and \u03bb2 parameters, facilitating optimal model tuning and enhancing predictive accuracy.<\/span><\/li>\n<\/ul>\n<h4 id=\"applications-of-elastic-net-regularisation\"><span class=\"ez-toc-section\" id=\"Applications_of_Elastic_Net_Regularisation\"><\/span><b>Applications of Elastic Net Regularisation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Elastic Net regularisation finds extensive application in fields like genomics and financial modelling due to its effectiveness in handling datasets with numerous predictors and potential multicollinearity issues.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In genomics, where vast amounts of genetic data are analysed, Elastic Net aids in identifying relevant genetic markers while managing correlations between them. Similarly, in financial modelling, especially portfolio optimisation, Elastic Net helps select robust sets of financial instruments while considering their interdependencies.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Its ability to balance between L1 (Lasso) and L2 (Ridge) penalties makes it particularly suitable for scenarios where feature selection and regularisation are critical for model stability and interpretability.<\/span><\/p>\n<h3 id=\"dropout-regularisation\"><span class=\"ez-toc-section\" id=\"Dropout_Regularisation\"><\/span><b>Dropout Regularisation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><a href=\"https:\/\/medium.com\/analytics-vidhya\/a-simple-introduction-to-dropout-regularization-with-code-5279489dda1e\"><span style=\"font-weight: 400;\">Dropout<\/span><\/a><span style=\"font-weight: 400;\"> is a regularisation technique used primarily in neural networks. It involves randomly &#8220;dropping out&#8221; a fraction of the neurons during training, which prevents the network from becoming too reliant on particular neurons and encourages the network to learn more robust features.<\/span><\/p>\n<h4 id=\"how-dropout-regularisation-works\"><span class=\"ez-toc-section\" id=\"How_Dropout_Regularisation_Works\"><\/span><b>How Dropout Regularisation Works<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Dropout regularisation is a powerful technique for training neural networks to prevent overfitting. During each training iteration, a specified fraction ???? of the neurons in the network is randomly set to zero. This stochastic process forces the network to learn redundant data representations, as it cannot rely heavily on any single neuron.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The fraction ???? is a hyperparameter that can be adjusted based on the complexity of the network and the dataset characteristics. This adjustment allows for flexibility in how aggressively or conservatively dropout is applied.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Notably, during testing or inference, dropout is not applied. Instead, the outputs of the neurons are scaled by 1 \u2014 ????. This scaling compensates for the neurons that were dropped out during training, ensuring that the expected output of the network remains consistent across both training and testing phases.<\/span><\/p>\n<h4 id=\"advantages-of-dropout-regularisation\"><span class=\"ez-toc-section\" id=\"Advantages_of_Dropout_Regularisation\"><\/span><b>Advantages of Dropout Regularisation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Dropout regularisation is a powerful technique in neural networks designed to combat overfitting and enhance generalisation. Dropouts introduce robustness into the network&#8217;s learning process by randomly disabling neurons during training. This approach offers several advantages:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Preventing Overfitting: <\/b><span style=\"font-weight: 400;\">Dropout mitigates overfitting by preventing neurons from co-adapting too much, ensuring that no single neuron dominates the network&#8217;s learning.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Improving Generalisation: <\/b><span style=\"font-weight: 400;\">By forcing the network to learn redundant representations, dropout encourages the discovery of more diverse features and patterns. This diversification enhances the model&#8217;s ability to generalise unseen data effectively, leading to more reliable predictions in real-world applications.<\/span><\/li>\n<\/ul>\n<h4 id=\"applications-of-dropout-regularisation\"><span class=\"ez-toc-section\" id=\"Applications_of_Dropout_Regularisation\"><\/span><b>Applications of Dropout Regularisation<\/b><span class=\"ez-toc-section-end\"><\/span><\/h4>\n<p><span style=\"font-weight: 400;\">Dropout regularisation has found widespread application in various domains of deep learning, prominently in convolutional neural networks (CNNs) and recurrent neural networks (RNNs). Its effectiveness extends notably to critical tasks such as enhancing accuracy in image recognition by preventing overfitting and improving robustness in speech recognition systems.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In <\/span><a href=\"https:\/\/pickl.ai\/blog\/introduction-to-natural-language-processing\/\"><span style=\"font-weight: 400;\">natural language processing<\/span><\/a><span style=\"font-weight: 400;\"> (NLP), dropout mitigates the risk of model reliance on specific word embeddings or sequences, thereby enhancing the generalisation capability of NLP models across diverse textual inputs. These applications underscore dropout&#8217;s versatility in fostering more reliable and adaptable deep learning architectures across different fields of artificial intelligence.<\/span><\/p>\n<h2 id=\"choosing-the-right-regularisation-technique\"><span class=\"ez-toc-section\" id=\"Choosing_the_Right_Regularisation_Technique\"><\/span><b>Choosing the Right Regularisation Technique<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-11891\" src=\"https:\/\/pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image9.jpg\" alt=\"\" width=\"1000\" height=\"333\" srcset=\"https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image9.jpg 1000w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image9-300x100.jpg 300w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image9-768x256.jpg 768w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image9-110x37.jpg 110w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image9-200x67.jpg 200w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image9-380x127.jpg 380w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image9-255x85.jpg 255w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image9-550x183.jpg 550w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image9-800x266.jpg 800w, https:\/\/www.pickl.ai\/blog\/wp-content\/uploads\/2023\/12\/image9-150x50.jpg 150w\" sizes=\"(max-width: 1000px) 100vw, 1000px\" \/><\/p>\n<p><span style=\"font-weight: 400;\">Choosing the right regularisation technique is critical in Machine Learning, as it directly impacts the model&#8217;s performance and ability to generalise to new data. Each regularisation method\u2014L1, L2, Elastic Net, and Dropout\u2014has specific strengths that align with data characteristics and modelling objectives.<\/span><\/p>\n<p><b>When deciding on the appropriate regularisation technique:<\/b><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Use L1 Regularisation (Lasso):<\/b><span style=\"font-weight: 400;\"> Opt for L1 regularisation when you suspect only a subset of features are crucial for prediction. By penalising the absolute values of coefficients, L1 regularisation encourages sparsity in the model, effectively performing feature selection. This is beneficial when dealing with high-dimensional data where feature relevance varies widely.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Use L2 Regularisation (Ridge): <\/b><span style=\"font-weight: 400;\">Choose L2 regularisation when you want to include all features in the model but prevent them from over-influencing predictions, especially in multicollinearity. L2 regularisation penalises the squared magnitudes of coefficients, promoting smoother model outputs and reducing the impact of correlated features.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Use Elastic Net: <\/b><span style=\"font-weight: 400;\">Employ Elastic Net when your dataset contains many correlated features. By combining L1 and L2 penalties, Elastic Net leverages the strengths of both methods, effectively handling multicollinearity while allowing for feature selection. This makes it a robust choice for datasets with complex relationships among predictors.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Use Dropout:<\/b><span style=\"font-weight: 400;\"> Implement Dropout specifically in neural networks to combat overfitting and enhance generalisation. By randomly dropping neurons during training, dropout forces the network to learn redundant representations and prevents it from relying too heavily on specific neurons, thereby improving model robustness.<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">By understanding these guidelines and assessing the specific characteristics of your data\u2014such as feature importance, multicollinearity, and neural network architecture\u2014you can strategically select the regularisation technique that best suits your modelling goals and improves your model&#8217;s performance on unseen data.<\/span><\/p>\n<p><b>Further Read: <\/b><b><br \/>\n<\/b><a href=\"https:\/\/pickl.ai\/blog\/ai-and-machine-learning-courses\/\"><span style=\"font-weight: 400;\">Discover Best AI and Machine Learning Courses For Your Career<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<p><a href=\"https:\/\/pickl.ai\/blog\/machine-learning-interview-questions-ace-your-next-interview\/\"><span style=\"font-weight: 400;\">Machine Learning Interview Questions: Ace Your Next Interview<\/span><\/a><span style=\"font-weight: 400;\">.<\/span><\/p>\n<h2 id=\"frequently-asked-questions\"><span class=\"ez-toc-section\" id=\"Frequently_Asked_Questions\"><\/span><b>Frequently Asked Questions<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3 id=\"what-is-regularisation-in-machine-learning-2\"><span class=\"ez-toc-section\" id=\"What_is_regularisation_in_Machine_Learning\"><\/span><b>What is regularisation in Machine Learning?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Regularisation in Machine Learning involves adding a penalty to the model&#8217;s loss function to prevent overfitting. It adjusts the complexity of the model by penalising large coefficients, thereby promoting simpler models that generalise better to unseen data, improving overall predictive performance.<\/span><\/p>\n<h3 id=\"which-regularisation-technique-is-best-for-feature-selection\"><span class=\"ez-toc-section\" id=\"Which_regularisation_technique_is_best_for_feature_selection\"><\/span><b>Which regularisation technique is best for feature selection?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">L1 regularisation, known as Lasso, is highly effective for feature selection. It adds a penalty equal to the absolute value of coefficients, encouraging some coefficients to be precisely zero. This feature selection capability simplifies models by focusing on the most relevant features, enhancing interpretability and reducing overfitting.<\/span><\/p>\n<h3 id=\"how-does-dropout-regularisation-improve-neural-networks\"><span class=\"ez-toc-section\" id=\"How_does_dropout_regularisation_improve_neural_networks\"><\/span><b>How does dropout regularisation improve neural networks?<\/b><span class=\"ez-toc-section-end\"><\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Dropout regularisation improves neural networks by randomly deactivating neurons during training. This technique prevents neurons from co-adapting too much to specific features, promoting the learning of diverse features. By forcing the network to be more robust, dropout reduces overfitting. It enhances the model&#8217;s ability to generalise to new data.<\/span><\/p>\n<h2 id=\"conclusion\"><span class=\"ez-toc-section\" id=\"Conclusion\"><\/span><b>Conclusion<\/b><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Regularisation techniques like L1, L2, Elastic Net, and Dropout are crucial in improving Machine Learning model performance. By balancing model complexity and preventing overfitting or underfitting, these methods ensure robust predictions of new data.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">L1 and L2 facilitate feature selection and control coefficient magnitudes, while Elastic Net blends their benefits for handling multicollinearity. Dropout, primarily for neural networks, enhances generalisation by diversifying feature learning. Understanding these techniques empowers Data Scientists to select the most suitable method based on data characteristics, fostering reliable and scalable Machine Learning models.<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"Discover essential Machine Learning regularisation techniques: L1, L2, Elastic Net, and Dropout.\n","protected":false},"author":28,"featured_media":11892,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"om_disable_all_campaigns":false,"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[2],"tags":[1997,1998],"ppma_author":[2218,2185],"class_list":{"0":"post-5512","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-machine-learning","8":"tag-regularization-in-machine-learning","9":"tag-types-of-regularization-in-machine-learning"},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.3 (Yoast SEO v27.0) - https:\/\/yoast.com\/product\/yoast-seo-premium-wordpress\/ -->\n<title>Regularization in Machine Learning: All you need to know<\/title>\n<meta name=\"description\" content=\"Learn about regularisation in 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