Decision Trees: Rules the Model Writes Itself

How a decision tree splits on the feature and threshold that most reduces impurity at each node, a runnable scikit-learn example on phishing-URL features, and why an unconstrained tree memorises rather than generalises.

~5 min total·4 quadrants of structured content

By the end of this topic, you will

Describe the five-step procedure a decision tree uses to split its data
Train a DecisionTreeClassifier in scikit-learn with max_depth and min_samples_leaf set
Explain overfitting in a decision tree and name two hyperparameters that limit it

Quadrant 1 · e-Tutorial

Video lectures and walkthroughs

Video content coming soon.

Quadrant 2 · e-Content

Articles and case studies

Decision Trees: Rules the Model Writes Itself

A decision tree learns, entirely on its own from data, which question to ask first. Left unconstrained, it will also learn to memorise the training set instead of the pattern inside it.

Quadrant 3 · Web Resources