{"id":37,"date":"2026-05-18T12:37:24","date_gmt":"2026-05-18T12:37:24","guid":{"rendered":"https:\/\/convly.ai\/top-10-machine-learning-algorithms\/"},"modified":"2026-06-10T05:06:16","modified_gmt":"2026-06-10T05:06:16","slug":"top-10-machine-learning-algorithms","status":"publish","type":"post","link":"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/","title":{"rendered":"Top 10 Machine Learning Algorithms Every Beginner Should Know"},"content":{"rendered":"<p>Machine learning has hundreds of algorithms, but a working data scientist relies on a surprisingly small core set. Learn these 10 well and you can handle the large majority of real-world problems. This guide explains each one in plain language \u2014 what it does, the idea behind it, and when to use it \u2014 no heavy math.<\/p>\n<div class=\"convly-tldr\">\n<h3>Punti chiave<\/h3>\n<ul>\n<li><strong>You don&#8217;t need hundreds of algorithms<\/strong> \u2014 about ten cover most practical work.<\/li>\n<li><strong>Start simple:<\/strong> linear and logistic regression are the foundation and often hard to beat.<\/li>\n<li><strong>Tree-based methods<\/strong> (random forests, gradient boosting) are the workhorses for structured data.<\/li>\n<li><strong>Match the algorithm to the problem<\/strong> \u2014 there is no single best one.<\/li>\n<\/ul>\n<\/div>\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_84 counter-flat ez-toc-counter ez-toc-container-direction\">\n<label for=\"ez-toc-cssicon-toggle-item-6a38a8d251928\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Attiva\/Disattiva<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #000000;color:#000000\" 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: #000000;color:#000000\" 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-6a38a8d251928\"  aria-label=\"Attiva\/Disattiva\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1' ><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#1_Linear_regression\" >1. Linear regression<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#2_Logistic_regression\" >2. Logistic regression<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#3_Decision_trees\" >3. Decision trees<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#4_Random_forest\" >4. Random forest<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#5_Gradient_boosting\" >5. Gradient boosting<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#6_Support_vector_machines_SVM\" >6. Support vector machines (SVM)<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#7_K-nearest_neighbors_KNN\" >7. K-nearest neighbors (KNN)<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#8_K-means_clustering\" >8. K-means clustering<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#9_Naive_Bayes\" >9. Naive Bayes<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#10_Neural_networks\" >10. Neural networks<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-11\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#Which_algorithm_should_you_use\" >Which algorithm should you use?<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-12\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#How_to_actually_choose_a_quick_evaluation_workflow\" >How to actually choose: a quick evaluation workflow<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-13\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#FAQ\" >Domande frequenti<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-14\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#Bottom_line\" >Conclusione<\/a><\/li><li class='ez-toc-page-1'><a class=\"ez-toc-link ez-toc-heading-15\" href=\"https:\/\/convly.ai\/it\/top-10-machine-learning-algorithms\/#Related_articles\" >Articoli correlati<\/a><\/li><\/ul><\/nav><\/div>\n<h2><span class=\"ez-toc-section\" id=\"1_Linear_regression\"><\/span>1. Linear regression<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>What it does:<\/strong> predicts a number by fitting a straight-line relationship between inputs and the output.<\/p>\n<p><strong>The idea:<\/strong> find the line that best fits your data points. Predict a house price from its size, or sales from ad spend \u2014 linear regression draws the trend and reads predictions off it.<\/p>\n<p><strong>Use it for:<\/strong> predicting continuous values when the relationship is roughly linear. It&#8217;s simple, fast, and easy to explain \u2014 always a sensible first attempt.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"2_Logistic_regression\"><\/span>2. Logistic regression<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>What it does:<\/strong> predicts a category \u2014 usually a yes\/no \u2014 by estimating a probability.<\/p>\n<p><strong>The idea:<\/strong> despite the name, it&#8217;s a classification algorithm. It weighs the inputs and outputs a probability between 0 and 1: will this customer churn? Is this email spam?<\/p>\n<p><strong>Use it for:<\/strong> binary classification. Like linear regression, it&#8217;s simple, fast, interpretable, and a strong baseline.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"3_Decision_trees\"><\/span>3. Decision trees<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>What it does:<\/strong> makes predictions by asking a sequence of yes\/no questions.<\/p>\n<p><strong>The idea:<\/strong> it builds a flowchart. &#8220;Is income above X? \u2192 Is age below Y? \u2192 &#8230;&#8221; Each branch narrows things down until it reaches a decision.<\/p>\n<p><strong>Use it for:<\/strong> classification and regression when you want a model a human can read and follow. The weakness: a single tree easily overfits \u2014 which the next two algorithms fix.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"4_Random_forest\"><\/span>4. Random forest<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>What it does:<\/strong> combines many decision trees into one stronger, more reliable model.<\/p>\n<p><strong>The idea:<\/strong> instead of trusting one tree, build hundreds \u2014 each slightly different \u2014 and let them vote. The crowd is more accurate and far more stable than any individual tree.<\/p>\n<p><strong>Use it for:<\/strong> a huge range of classification and regression tasks on structured data. It&#8217;s accurate, robust, and forgiving \u2014 one of the best general-purpose algorithms to reach for.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"5_Gradient_boosting\"><\/span>5. Gradient boosting<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>What it does:<\/strong> builds trees in sequence, each one correcting the mistakes of the last.<\/p>\n<p><strong>The idea:<\/strong> rather than building trees independently (as a random forest does), build them one after another, each focused on the errors still remaining. The result is often extremely accurate.<\/p>\n<p><strong>Use it for:<\/strong> structured\/tabular data when you want top accuracy. Popular implementations (such as XGBoost and LightGBM) consistently win data-science competitions. It needs more careful tuning than a random forest.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"6_Support_vector_machines_SVM\"><\/span>6. Support vector machines (SVM)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>What it does:<\/strong> classifies by finding the best dividing boundary between groups.<\/p>\n<p><strong>The idea:<\/strong> it draws the line \u2014 or, in higher dimensions, the surface \u2014 that separates the categories with the widest possible margin between them.<\/p>\n<p><strong>Use it for:<\/strong> classification on small or medium datasets, especially with many features. Powerful, though less commonly the first choice now that tree-based methods dominate tabular data.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"7_K-nearest_neighbors_KNN\"><\/span>7. K-nearest neighbors (KNN)<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>What it does:<\/strong> classifies a new item by looking at the items most similar to it.<\/p>\n<p><strong>The idea:<\/strong> &#8220;you resemble your neighbors.&#8221; To classify a new point, find the <em>k<\/em> closest known points and take their majority label. There&#8217;s no real training phase \u2014 it just compares.<\/p>\n<p><strong>Use it for:<\/strong> simple classification problems and recommendation-style tasks. Intuitive and easy to grasp, but slow on large datasets.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"8_K-means_clustering\"><\/span>8. K-means clustering<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>What it does:<\/strong> automatically groups data into <em>k<\/em> clusters \u2014 without any labels.<\/p>\n<p><strong>The idea:<\/strong> this is an <a href=\"\/it\/supervised-vs-unsupervised-vs-reinforcement-learning\/\">unsupervised algorithm<\/a>. Tell it how many groups to find, and it sorts the data into that many natural clusters by similarity.<\/p>\n<p><strong>Use it for:<\/strong> discovering structure in unlabeled data \u2014 customer segmentation, grouping documents, organizing data for exploration.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"9_Naive_Bayes\"><\/span>9. Naive Bayes<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>What it does:<\/strong> classifies using probability and Bayes&#8217; theorem.<\/p>\n<p><strong>The idea:<\/strong> it calculates the probability of each category given the input&#8217;s features, assuming (naively, but usefully) that the features are independent. Despite that simplifying assumption, it works remarkably well.<\/p>\n<p><strong>Use it for:<\/strong> text classification especially \u2014 spam filtering, sentiment analysis, topic sorting. It&#8217;s fast, light, and a strong baseline for language tasks.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"10_Neural_networks\"><\/span>10. Neural networks<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p><strong>What it does:<\/strong> learns very complex patterns through layers of connected units.<\/p>\n<p><strong>The idea:<\/strong> covered in depth in our <a href=\"\/it\/neural-networks-explained\/\">neural networks guide<\/a> \u2014 layers of simple units that learn features automatically. Deep neural networks are the basis of <a href=\"\/it\/deep-learning-vs-machine-learning\/\">deep learning<\/a>.<\/p>\n<p><strong>Use it for:<\/strong> complex, unstructured data \u2014 images, audio, language. For simple structured data, the algorithms above are often faster and just as good.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Which_algorithm_should_you_use\"><\/span>Which algorithm should you use?<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<table class=\"convly-vs\">\n<thead>\n<tr>\n<th>Your problem<\/th>\n<th>Start with<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Predicting a number<\/td>\n<td>Linear regression, then gradient boosting<\/td>\n<\/tr>\n<tr>\n<td>Yes\/no classification<\/td>\n<td>Logistic regression, then random forest<\/td>\n<\/tr>\n<tr>\n<td>Structured\/tabular data, max accuracy<\/td>\n<td>Gradient boosting or random forest<\/td>\n<\/tr>\n<tr>\n<td>Grouping unlabeled data<\/td>\n<td>K-means clustering<\/td>\n<\/tr>\n<tr>\n<td>Text classification<\/td>\n<td>Naive Bayes<\/td>\n<\/tr>\n<tr>\n<td>Images, audio, language<\/td>\n<td>Neural networks<\/td>\n<\/tr>\n<tr>\n<td>You want an explainable model<\/td>\n<td>Decision tree, linear\/logistic regression<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The professional&#8217;s habit: <strong>start simple<\/strong>. Try linear or logistic regression first to set a baseline, then move to a random forest or gradient boosting if you need more accuracy. Reach for neural networks when the data is genuinely complex and unstructured. A simple model that you understand often beats a complex one you don&#8217;t.<\/p>\n<p><!--ai-enriched--><\/p>\n<h2><span class=\"ez-toc-section\" id=\"How_to_actually_choose_a_quick_evaluation_workflow\"><\/span>How to actually choose: a quick evaluation workflow<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>Knowing what each algorithm does is only half the job. In practice you rarely pick the &#8220;right&#8221; algorithm by reasoning alone \u2014 you pick two or three plausible candidates and let your data decide. Here is the workflow professionals use, and it takes minutes once your data is clean.<\/p>\n<p><strong>1. Start with a dumb baseline.<\/strong> Before any fancy model, measure how a trivial predictor performs \u2014 always guessing the majority class, or always predicting the average value. If your real model can&#8217;t comfortably beat that, something is wrong with your features or your data, not your choice of algorithm. A baseline turns &#8220;is 82% accuracy good?&#8221; into a question you can answer.<\/p>\n<p><strong>2. Try a small shortlist, not everything.<\/strong> For most tabular problems, three candidates cover the field: logistic or linear regression (fast, interpretable, a strong baseline in its own right), a random forest (robust, almost no tuning), and a gradient boosting model (usually the top performer on structured data). Train all three and compare. You learn more from one honest bake-off than from weeks of theorizing.<\/p>\n<p><strong>3. Score with cross-validation, not a single split.<\/strong> A single train\/test split can flatter or punish a model by luck. K-fold cross-validation \u2014 splitting the data into folds, training on most and testing on the rest, then rotating \u2014 gives a far more honest estimate. In scikit-learn, the standard Python library for this, a single <strong>cross_val_score<\/strong> call does it in one line and defaults to a sensible five folds.<\/p>\n<p><strong>4. Pick the metric that matches the stakes.<\/strong> Accuracy is misleading whenever classes are imbalanced: a fraud detector that calls everything &#8220;legitimate&#8221; can be 99% accurate and useless. Choose deliberately \u2014 precision and recall (or their balance, the F1 score) for imbalanced classification, and a measure like mean absolute error for regression. The metric, not the algorithm, is what your project is actually optimizing.<\/p>\n<p><strong>When to let AutoML do it.<\/strong> If you would rather not hand-run the bake-off, tools such as AutoGluon, Auto-sklearn, and TPOT will test many algorithms and hyperparameters and hand back the best ensemble. They are excellent for supervised tabular problems and a fast way to set a high bar. Their limits are worth knowing: they add compute cost, the winning model is often a hard-to-explain ensemble, and they still don&#8217;t meaningfully cover unsupervised or reinforcement learning \u2014 so the judgment in this article remains yours to apply.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"FAQ\"><\/span>Domande frequenti<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<h3>What are the most important machine learning algorithms?<\/h3>\n<p>For most practical work: linear regression, logistic regression, decision trees, random forests, gradient boosting, support vector machines, k-nearest neighbors, k-means clustering, naive Bayes, and neural networks. These ten cover the large majority of real-world problems.<\/p>\n<h3>Which machine learning algorithm should a beginner learn first?<\/h3>\n<p>Start with linear regression and logistic regression. They are the simplest, easiest to understand, fast to run, and they teach the core ideas \u2014 fitting a model to data and making predictions \u2014 that every other algorithm builds on.<\/p>\n<h3>What is the best machine learning algorithm?<\/h3>\n<p>There is no single best algorithm \u2014 the right choice depends on the problem, the data, and your goals. For structured data, gradient boosting and random forests are usually top performers. For images and language, neural networks lead. Always match the algorithm to the task.<\/p>\n<h3>Do I need to know the math behind these algorithms?<\/h3>\n<p>To use them with modern libraries, you need only a conceptual understanding of what each does and when to apply it. To tune them expertly or do research, deeper math helps. Many people start by applying algorithms and learn the math gradually.<\/p>\n<h3>What is the difference between an algorithm and a model?<\/h3>\n<p>An algorithm is the method or procedure for learning from data \u2014 like linear regression or random forest. A model is the result: the trained output produced when you run an algorithm on a specific dataset. The algorithm is the recipe; the model is the finished dish.<\/p>\n<h3>How many machine learning algorithms do I actually need to know?<\/h3>\n<p>Fewer than you&#8217;d think. For most real tabular problems, three families do the heavy lifting: linear and logistic regression as fast, interpretable baselines; random forests for robust results with little tuning; and gradient boosting, which tends to win on structured data. Master those deeply, understand clustering and KNN at a conceptual level, and you can solve the large majority of everyday problems before ever reaching for a neural network.<\/p>\n<h3>Should I just use AutoML instead of learning these algorithms?<\/h3>\n<p>AutoML is a genuine shortcut for supervised tabular tasks \u2014 frameworks like AutoGluon will trial many algorithms and return a strong ensemble with little effort. But it is not a replacement for understanding. You still have to frame the problem, choose the right evaluation metric, clean and engineer features, and judge whether the result is trustworthy. AutoML also barely touches unsupervised and reinforcement learning. Treat it as a tool that runs the bake-off for you, not as a substitute for knowing what the contestants are.<\/p>\n<h3>Which algorithm wins most machine learning competitions?<\/h3>\n<p>On the structured, tabular datasets that dominate platforms like Kaggle, gradient boosting \u2014 typically via XGBoost, LightGBM, or CatBoost \u2014 is the overwhelming favorite, usually as part of an ensemble. Deep neural networks lead instead on unstructured data such as images, audio, and text. The pattern is consistent: reach for boosting on tables, and for neural networks when the input is raw perception.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"Bottom_line\"><\/span>Conclusione<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>You don&#8217;t need to know hundreds of algorithms to do real machine learning \u2014 you need these ten. The simple ones (linear and logistic regression) are your baselines and are often hard to beat. The tree-based methods (random forests, gradient boosting) are the workhorses for structured data. K-means handles unlabeled grouping, naive Bayes handles text, and neural networks handle the complex, unstructured problems.<\/p>\n<p>The skill isn&#8217;t memorizing algorithms \u2014 it&#8217;s matching the right one to the problem, and starting simple. Learn these ten, practice on <a href=\"\/it\/best-free-datasets-machine-learning\/\">real datasets<\/a>, and you can tackle the large majority of machine learning work.<\/p>\n<p><!--related-block--><\/p>\n<div class=\"convly-related\">\n<h2><span class=\"ez-toc-section\" id=\"Related_articles\"><\/span>Articoli correlati<span class=\"ez-toc-section-end\"><\/span><\/h2>\n<ul>\n<li><a href=\"https:\/\/convly.ai\/it\/overfitting-how-to-prevent-it\/\">Overfitting nell'apprendimento automatico: cos'\u00e8 e come prevenirlo<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/it\/best-free-datasets-machine-learning\/\">15 Best Free Datasets for Machine Learning Projects (2026)<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/it\/neural-networks-explained\/\">Reti neurali spiegate a non ingegneri (Guida 2026)<\/a><\/li>\n<li><a href=\"https:\/\/convly.ai\/it\/deep-learning-vs-machine-learning\/\">Deep learning vs apprendimento automatico: le differenze fondamentali (2026)<\/a><\/li>\n<\/ul>\n<\/div>","protected":false},"excerpt":{"rendered":"<p>The 10 machine learning algorithms that matter most \u2014 explained in plain language, with what each one does and when to reach for it. The essential beginner&#8217;s map.<\/p>","protected":false},"author":0,"featured_media":38,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[2],"tags":[474,472,30,471,473],"class_list":["post-37","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning","tag-data-science","tag-linear-regression","tag-machine-learning-algorithms","tag-ml-algorithms","tag-random-forest"],"_links":{"self":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/37","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/types\/post"}],"replies":[{"embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/comments?post=37"}],"version-history":[{"count":3,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/37\/revisions"}],"predecessor-version":[{"id":1053,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/posts\/37\/revisions\/1053"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/media\/38"}],"wp:attachment":[{"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/media?parent=37"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/categories?post=37"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/convly.ai\/it\/wp-json\/wp\/v2\/tags?post=37"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}