Classification and Ensemble Learning

Why this module belongs in the course

An accurate classifier can still be unusable when its probabilities are poorly calibrated, its errors have unequal cost or its behaviour changes across important groups.

Before you begin

The concepts and project evidence from Module 4. You should also be able to create a Python virtual environment and keep private or employer data out of the exercise.

Four lessons, one connected result

1. Lesson 1Decision Trees: ID3, C4.5 and CART Explained85 min · Intermediate
2. Lesson 2Compare Logistic Regression, Naive Bayes and SVM90 min · Intermediate
3. Lesson 3Bagging, Random Forests and Gradient Boosting95 min · Intermediate
4. Lesson 4Project: Benchmark Classifiers and Diagnose Their Errors160 min · Intermediate

How to know you are ready to continue

Complete the checkpoint without copying the worked example. Keep the code, output and a short decision note. Your note should explain one choice, one failure you observed and one limitation a reviewer should know.

Primary references for this module

The lessons explain the ideas in original wording. Use these primary or official sources when a library interface, standard or research claim needs verification.

• scikit-learn decision trees
• scikit-learn permutation importance
• scikit-learn linear models
• scikit-learn Naive Bayes
• scikit-learn SVM
• scikit-learn ensemble methods
• XGBoost documentation
• LightGBM documentation