Boosting

The second ensemble technique we'll be looking at is boosting, which involves incrementally training new models that focus on the misclassified data points in the previous model and utilizes weighted averages to turn weak models (underfitting models having high bias) into stronger models. Unlike bagging, where each base estimator could be trained independently of the others, the training of each base estimator in a boosted algorithm depends on the previous one.

Although boosting also uses the concept of bootstrapping, it's done differently from bagging, since each sample of data is weighted, implying that some bootstrapped samples can be used for training more often than other samples. When training each model, the algorithm keeps track of which features are most useful and which data samples have the most prediction error; these are given higher weightage and are considered to require more iterations to properly train the model.

When predicting the output, the boosting ensemble takes...