{"id":178163,"date":"2024-03-01T15:12:31","date_gmt":"2024-03-01T14:12:31","guid":{"rendered":"https:\/\/liora.io\/en\/?p=178163"},"modified":"2026-08-09T19:38:49","modified_gmt":"2026-08-09T18:38:49","slug":"bagging-machine-learning-what-is-it-about","status":"publish","type":"post","link":"https:\/\/liora.io\/en\/bagging-machine-learning-what-is-it-about","title":{"rendered":"Bagging Machine Learning: What is it about?"},"content":{"rendered":"\n

Bagging Machine Learning: “Together we’re stronger” , bagging could be symbolised by this quote. In fact, this technique is one of the ensemble methods, which consists of considering a set of models in order to make the final decision. Let’s take a closer look at bagging.<\/strong><\/p>\n\n\n

First of all, we need to work on the data. We’re not going to provide all our models with the same data, because we want our models to be independent. The problem is that we can’t partition the dataset<\/a>. For a large number of models, our models would not be sufficiently trained and would produce mediocre results.<\/strong><\/p>\n\n\n

The solution to our problems: bootstrapping<\/h2>\n\n\n

To overcome this problem, we \u00a0create a bootstrap dataset.<\/a> With this method, we create a new dataset from the initial dataset. The new dataset is the same size as the initial one. Let n be the sample size and let E_1 be the initial dataset and E_2 the bootstrapped dataset<\/a>.<\/p>\n\n\n

From E_1, we will randomly select an individual and place it in E_2. We will repeat this step until E_2 is of size n. It’s important to note that the elements chosen are always in E_1, so we can select the same element several times. It is this feature that allows us to produce different data.<\/strong><\/p>\n\n\n

Let’s take a look at a concrete example, using the same notation as above.<\/p>\n\n\n

\"Illustration<\/figure>\n\n\n
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