Introduction to Boosting: Implementing AdaBoost in Python

In Machine Learning context, there are typically two kinds of learners or algorithms, ones that learn well the correlations and gives out strong predictions and the ones which are lazy and gives out average predictions that are slightly better than random selection or guessing. The algorithms that fall into the former category are referred to as strong learners and the ones that fall into the latter are called weak or lazy learners. Boosting essentially is an ensemble learning method to boost the performances or efficiency of weak learners to convert them into stronger ones. Boosting simply creates a strong classifier or regressor from a number of weak classifiers or regressors by learning from the incorrect predictions of weak classifiers or regressors. The Simple  Intuition Behind