Practical Big Data Analytics

Boosting algorithms

Boosting is a technique that uses weights and a set of weak learners, such as decision trees, in order to improve model performance. Boosting assigns weights to data based on model misclassification and future learner's (created during the boosting machine learning process) focus on the misclassified examples. Examples that were correctly classified will be reassigned new weights which will generally be lower than those that were not correctly classified. The weight can be based on a cost function, such as a majority vote, using subsets of the data.

In simple and non-technical terms, boosting uses a series of weak learners, and each learner 'learns' from the mistakes of the prior learners.

Boosting is generally more popular ...