thinQmaster Virtual testing partner Adaptive practice exams that find your weak spots and drill them. Every answer explained. From $69
Enroll

thinQmaster

Find your exam

Five questions free, before you decide.

Step 1 · choose a vendor

In one of our camps? Your practice exams come with the tuition — they are on your study page already.

AI & Machine Learning · Intermediate

Boosting

An ensemble method that trains weak learners sequentially, weighting each toward the errors of its predecessors.

Definition

Boosting converts many weak learners into a strong learner by focusing successive models on hard examples. AdaBoost reweights misclassified samples, while gradient boosting fits residual gradients. Boosting reduces bias but can overfit noisy data if run too long.

Exam tip

Boosting is sequential and bias-reducing; it can be sensitive to noisy labels.

Related terms