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As in many real-world situations, union makes algorithms stronger. With this philosophy in mind, ensemble methods combine several weak classifiers into a massive one---in terms of accuracy. In the last post we learnt a primer with Random Forest . Therefore, the next cornerstone is gradient boosting. I mentioned Gradient Boosting many times in this blog, but I only commented the fundamental ideas, without discussing further the details. In this entry I will share my two cents. Let me introduce a little bit of history, first: recall the Kaggle-Higgs competition . The top scores in the leaderboard have been obtained by using distinct forms of gradient boosting, and XGBoost is the direct responsible of many of these. The question is, hence, how does this algorithm work ? Figure 1. A high-level description of the Gradient Boosting method I programmed. Click to enlarge. Informally, Gradient Boosting generates a sequence of classifiers in the form of an additive expansion, that i...

Several machine learning techniques foster the cooperation between subsolutions for obtaining an accurate outcome by combining many of them. Michigan-style LCSs are one of these families. However, the most well known are those that implement Boosting, and AdaBoost is the most successful of them (or, at least, the most studied one and the first to implement the ideas of Boosting). AdaBoost generates accurate predictions by combining several weak classifiers (also referred to as “the wisdom of the crowd”). The most outstanding fact about AdaBoost is that it is a deadly simple algorithm. The key of its success lies in the combination of many weak classifiers: these are very limited and their error rate is just slightly better than a random choice (hence their name). The typical implementation uses decision stumps (i.e., binary trees) that minimize the error between the prediction and the ground truth (that is, the desired outcome), so the classifiers have the form " if variable_...