AdaBoost Algorithm

AdaBoost fits a sequence of weak learners to the data. It then assigns more weight to incorrect predictions, and less weight to correct ones. This way the algorithm focuses more on observations that are harder to predict. The final result is obtained from the majority vote in classification, or the average in regression.

You can implement this algorithm using Scikit-learn. The n_estimators argument can be passed to it to indicate the number of weak learners needed. You can control the contribution of each weak learner using the learning_rate argument.

The algorithm uses decision trees as the base estimators by default. The base estimators and the parameters of the decision trees can be tuned to improve the performance of the model. By default, decision trees in AdaBoost have a single split.

Classification using AdaBoost

You can use the AdaBoostClassifier from Scikit-learn to implement the AdaBoost model for classification problems.