Machine Learning with Python

13

 

 

TREE-BASED METHODS

 

Tree-based learning algorithms, also known as Cart (Classification and Regression Trees), are a popular technique for predicting numeric and categorical outputs.

Tree-based methods, which include decision trees, bagging, random forests, and boosting, are considered highly effective in the space of supervised learning. This is partly due to their high accuracy and versatility as they can be used to predict both discrete and continuous outcomes.

 

Decision Trees

Decision trees create a decision structure to interpret patterns by splitting data into groups using variables that best split the data into homogenous or numerically relevant groups based on entropy (a measure of variance in the data among different ...