Demystifying Decision Tree Algorithms
Decision trees are intuitive models that recursively split data into smaller groups based on feature values. Each split aims to maximize homogeneity within branches while separating different classes.
Choosing the Best Split
Metrics like Gini impurity and entropy measure how mixed the classes are in each node. The algorithm searches over possible splits and selects the one that yields the largest reduction in impurity.
Preventing Overfitting
A tree grown until every leaf is pure often memorizes the training data. Pruning removes branches that provide little predictive power, leading to a simpler tree that generalizes better to new samples.
When to Use Decision Trees
Decision trees handle both numeric and categorical features and require minimal data preparation. They also serve as the building blocks for powerful ensemble methods like random forests and gradient boosting.
