Chapter 7: Supervised Learning¶

Master regression and classification—linear models, ensembles, SVMs, and tuning for production.

Metadata¶

Learning Objectives¶

• Build regression and classification models
• Apply regularization (L1, L2)
• Use decision trees, random forests, and gradient boosting
• Apply SVM and kernel methods
• Evaluate models with ROC, precision-recall, and regression metrics
• Tune hyperparameters effectively
• Handle imbalanced data and credit-risk scenarios

What's Included¶

Notebooks¶

Scripts¶

• supervised_toolkit.py — Core implementations and plotting utilities

Exercises¶

• 5 exercises with solutions (in solutions/ branch)

SVG Diagrams¶

• 3 visual diagrams for model architectures and evaluation

Read Online¶

You can read the full chapter content right here on the website:

• 07.1 Introduction -- Linear/polynomial regression, regularization (Ridge, Lasso)
• 07.2 Intermediate -- Classification, logistic regression, SVM, ROC curves
• 07.3 Advanced -- Ensemble methods, hyperparameter tuning, credit risk capstone

Or try the code in the Playground.

How to Use This Chapter¶

1. Clone and install dependencies

git clone https://github.com/luigipascal/berta-chapters.git
cd berta-chapters
pip install -r requirements.txt

2. Navigate to the chapter

cd chapters/chapter-07-supervised-learning

3. Launch Jupyter

jupyter notebook notebooks/01_introduction.ipynb

Created by Luigi Pascal Rondanini | Generated by Berta AI