
Ch.39: Decision Tree Hyperparameters
How max_depth, min_samples_split, min_samples_leaf, criterion, splitter, max_features, max_leaf_nodes, and min_impurity_decrease each pull an unconstrained decision tree back from overfitting, with real sklearn decision boundaries showing the underfit-to-overfit trade-off for each one.
Latest Posts
Ch.39: Decision Tree Hyperparameters
2026-08-31Machine Learning AlgorithmsCh.38: Entropy, Information Gain, and Gini Impurity
2026-08-31Machine Learning AlgorithmsCh.37: Decision Trees, the Core Intuition
2026-08-31Machine Learning AlgorithmsCh.36: The Kernel Trick, In Code
2026-08-30Machine Learning AlgorithmsCh.35: The Kernel Trick, Geometrically
2026-08-30Machine Learning AlgorithmsCh.34: Soft Margin SVM and the Hinge Loss
2026-08-30Series Catalog

Fundamental Machine Learning
Exploring the foundations of machine learning, from basic algorithms to advanced neural networks.

Machine Learning Algorithms
A deep dive into how core machine learning algorithms actually work, from linear regression to ensemble methods.

Networking Fundamentals
A beginner-to-advanced series on how computer networks actually work, from what happens during an HTTP request to DNS, IP addressing, and routing.

System Design Foundation
Building scalable systems, one concept at a time

Today I Learned
Unstructured notes, technical takeaways, and lightbulb moments from random technical videos and talks.
