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Module · 5 lessons
Trees & Ensembles
Master decision trees and the random forests and boosting methods built on top of them.
At a glance
Level
Intermediate
Lessons
5 lessons
Time to complete
1–2 weeks
Cost
Free forever · no sign-up
Tree-based models are among the most powerful and widely used algorithms for tabular data. You will learn how decision trees split data, how to control overfitting, and how ensembles like random forests combine many trees into a single strong predictor.
Lessons in this module
1
Foundations of Decision Trees
Understand what a decision tree is and how it learns yes/no rules from data, measuring node impurity with Gini and entropy on the real Adult Income dataset
2
Building Decision Trees with Scikit-Learn
Build, train, and read a decision tree classifier with scikit-learn on the real Adult Income dataset
3
Evaluating and Optimizing Decision Trees
Learn why decision trees overfit and how to control them by tuning max_depth, min_samples_leaf, and min_samples_split on the real Adult Income dataset
4
Cross-Validation and Ensemble Methods
Get honest performance estimates with k-fold cross-validation, then beat a single decision tree using bagging and random forests
5
Guided Project: Predicting Employee Productivity
Build an end-to-end regression project with decision trees and random forests on the real Garment Productivity dataset
Achievement
Complete all 5 lessons to finish the Trees & Ensembles module.