This module will focus on the ensemble methods decision trees, bagging, and random forests, which combine multiple models to improve prediction accuracy and reduce overfitting. Decision Trees are a ...

k-Nearest Neighbors makes sense on an intuitive level. Decision trees are a supervised learning model that can be used for either regression or classification tasks. In Module 2, we learned about the ...

Decision trees are at their heart a fairly simple type of classifier, and this is one of their advantages. Decision trees are constructed by analyzing a set of training examples for which the ...