Andreu Sancho Homepage

The Kaggle-Higgs competition has attracted my attention very much lately. In this particular challenge, the goal is to generate a predictive model out of a bunch of data taken from distinct LHC’s detectors. The numbers are the following: 250000 properly labeled instances, 30 real-valued features (containing missing values), and two classes, namely background { b } and signal { s }. Also, the site provides a test set consisting of 550000 unlabeled instances. There are nearly 1300 participants while I am writing this post, and a lot of distinct methods are put to the test to win the challenge. Very interestingly, ensemble methods are all in the head of the leaderboard, and the surprise is XGBoost , a gradient boosting method that makes use of binary trees.   After checking the computational horsepower of the XGBoost algorithm by myself, I decided to take a closer look at ensemble methods. To start with, I implemented a Random Forest , an algorithm that consists of many indep...

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_...