Tree.ExtraTreeClassifier

interface ExtraTreeProps {
    max_depth?: number;
    min_samples_split?: number;
    criterion?: 'entropy' | 'gini';
    max_features?: number;
}

constructor(props: ExtraTreeProps = {})

Example

const clf = new ExtraTreeClassifier();
clf.fit(X, Y);
const result = clf.predict(T);

Practical guide: ExtraTreeClassifier in JavaScript and TypeScript

ExtraTreeClassifier injects additional split randomness to reduce variance and improve generalization in noisy settings.

When to use ExtraTreeClassifier

• Standard decision trees overfit your training set.
• You need fast tree-based classification with stronger randomization.
• You plan to combine trees in ensemble-style workflows.

Implementation workflow

1. Train with randomized split behavior on cleaned tabular inputs.
2. Compare validation stability against DecisionTreeClassifier.
3. Tune depth and split constraints based on overfit indicators.

JavaScript deployment notes

• Prefer feature scaling for distance-based and gradient-based algorithms to improve stability.
• In browser apps, run heavy training in Web Workers to keep UI interactions smooth.
• Keep a simple baseline from the same module as a fallback model for comparison.

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