What you will learn¶
This module will present in details decision tree models. This model will be explained in both classification and regression problems. Besides, we will show which hyperparameters of the decision tree have an importance on their performance, allowing to find the best trade-off between under- and over-fit.
Before getting started¶
The required technical skills to carry on this module are:
skills acquired during the “The Predictive Modeling Pipeline” module with basic usage of scikit-learn;
skills acquired during the “Selecting The Best Model” module, mainly around the concept of underfit/overfit and the usage of cross-validation in scikit-learn.
Objectives and time schedule¶
The objective in the module are the following:
understand how decision trees are working in classification and regression;
check which tree parameters are important and their influences.
The estimated time to go through this module is about 3 hours.