{"id":6829,"date":"2021-12-10T07:44:28","date_gmt":"2021-12-10T06:44:28","guid":{"rendered":"https:\/\/immune.institute\/?p=6829"},"modified":"2021-12-10T07:44:28","modified_gmt":"2021-12-10T06:44:28","slug":"metodos-de-bagging-y-de-boosting-diferencia","status":"publish","type":"post","link":"https:\/\/immune.institute\/en\/blog\/metodos-de-bagging-y-de-boosting-diferencia\/","title":{"rendered":"Bagging and boosting methods: What is the difference?"},"content":{"rendered":"

Betting on a profession of the future is synonymous with betting on technology. And one of the safest bets for scientists is, without a doubt, the <\/span>Artificial Intelligence<\/span><\/a>. And within it, there is the branch of the <\/span>Machine Learning<\/b> which offers a multitude of professional opportunities.<\/span><\/p>\n

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Machine Learning is a technological speciality that straddles AI and Data Science and seeks to use data and algorithms to mimic human learning, improving its accuracy. <\/span><\/p>\n<\/blockquote>\n

These algorithms can predict almost any type of variable.<\/b>This is why we can use machine learning in all kinds of sectors of work. <\/span><\/p>\n

The learning of these machines or pieces of <\/span>software<\/span><\/a> is continuous. Gradually, they acquire more data and thus become more intelligent; they are able to understand human behaviour.<\/span><\/p>\n

Assembled machine learning algorithms<\/span><\/h2>\n

The <\/span>assembled algorithms or assemblies<\/b> are a type of machine learning algorithm that improves generalisation by using different combination strategies. <\/span><\/p>\n

In other words: <\/span>are the union of several simple algorithms that form a more complex and powerful one.<\/b>. <\/span><\/p>\n

It is worth noting that, although there are different types of ensemble algorithms such as majority voting, bagging, boosting or stacking in machine learning, in this post we only want to highlight <\/span>boosting and bagging.<\/b> Therefore, we will explain the bagging and boosting methods and their difference in detail below.<\/span><\/p>\n

Bagging<\/span><\/h2>\n

Firstly, it should be noted that both <\/span>bagging and boosting methods serve to reduce variance<\/b> (or variability of the data, with respect to the mean) in learning statistics.<\/span><\/p>\n

That said, bagging is an aggregation <\/span>Bootstrap<\/b> (a set of open source tools used in web development) that achieves the combination of different models, starting from an initial family, which reduces variance and avoids over-fitting. In other words, that <\/span>when we use bagging we are employing different machine learning models<\/b>.<\/span><\/p>\n

This methodology makes <\/span>predictive errors are compensated for<\/b>The model is trained on subsets - which choose samples with repetition, randomly - from the global training set. <\/span><\/p>\n

The bagging method is widely used with so-called decision trees.<\/b> Do you know what they consist of?<\/span><\/p>\n

Definition of Random Forest<\/span><\/h3>\n

First of all, <\/span>Decision trees are those prediction models made up of binary rules.<\/b>. In other words: yes or no. <\/span><\/p>\n

These decision trees form what are called Random Forest Bootstrap models or <\/span>random forests, combined with bagging<\/b>. In fact, their samples are somewhat different and the prediction is made from a new observation, which has been previously added to the individual trees of each model.<\/span><\/p>\n

These random forests are widely used in bagging, due to their performance and speed. <\/span><\/p>\n

Boosting<\/span><\/h2>\n

In contrast to bagging (which is known for its speed), boosting is a general methodology of <\/span>slow learning<\/b>. In this method, a wide variety of models that are obtained from a method with poor prediction are combined in order to produce a better predictor.<\/span><\/p>\n

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Thus, shallowly constructed decision trees, small and highly combinable trees, are used here. <\/span><\/p>\n<\/blockquote>\n

Likewise, <\/span>boosting is an attempt to fix the prediction errors of previous models.<\/b>. Sequenced trees seeking to improve on the previous classification.<\/span><\/p>\n

An additive model, where more weight is given to misclassified samples than to those that are well classified.<\/span><\/p>\n

Dedicate yourself to machine learning with IMMUNE<\/span><\/h2>\n

At the IMMUNE Institute of Technology we have different complementary trainings so that you will never ask again about \"bagging and boosting methods: what's the difference\". <\/span>At IMMUNE you will be able to become an expert in machine learning.<\/b><\/p>\n

To begin with, we have the <\/span>Degree in Software Development Engineering<\/span><\/a>as well as with our <\/span>Master in Data Science<\/span><\/a>with which you can become a <\/span>data scientist<\/span><\/a>. Also, if you prefer it by time, we have this one. <\/span>Data Analytics Bootcamp<\/span><\/a> or this one on <\/span>Voice Tech<\/span><\/a>Welcome to the training of the present and the future!  <\/span><\/p>","protected":false},"excerpt":{"rendered":"

Apostar por una profesi\u00f3n del futuro es sin\u00f3nimo de apostar por la tecnolog\u00eda. Y una de las apuestas m\u00e1s seguras (valga la redundancia) para los cient\u00edficos es, sin duda, la Inteligencia Artificial. Y dentro de ella, existe la rama del Machine Learning que ofrece multitud de salidas profesionales. Machine Learning es una especialidad tecnol\u00f3gica que […]<\/p>\n","protected":false},"author":3,"featured_media":7395,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_crdt_document":"","footnotes":""},"categories":[1],"tags":[],"class_list":["post-6829","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog"],"acf":[],"_links":{"self":[{"href":"https:\/\/immune.institute\/en\/wp-json\/wp\/v2\/posts\/6829","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/immune.institute\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/immune.institute\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/immune.institute\/en\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/immune.institute\/en\/wp-json\/wp\/v2\/comments?post=6829"}],"version-history":[{"count":0,"href":"https:\/\/immune.institute\/en\/wp-json\/wp\/v2\/posts\/6829\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/immune.institute\/en\/wp-json\/wp\/v2\/media\/7395"}],"wp:attachment":[{"href":"https:\/\/immune.institute\/en\/wp-json\/wp\/v2\/media?parent=6829"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/immune.institute\/en\/wp-json\/wp\/v2\/categories?post=6829"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/immune.institute\/en\/wp-json\/wp\/v2\/tags?post=6829"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}