API and practical guide for DecisionTreeClassifier in @kanaries/ml, including when to use it in JavaScript and TypeScript ML workflows.

Tree.DecisionTreeClassifier

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

constructor(props: DecisionTreeProps = {})

Example

const dt = new DecisionTreeClassifier({ criterion: 'gini' });
dt.fit(X, Y);
const result = dt.predict(T);

Practical guide: DecisionTreeClassifier in JavaScript and TypeScript

DecisionTreeClassifier learns human-readable if/else rules for classification tasks on tabular data.

When to use DecisionTreeClassifier

Interpretability and decision-path transparency are important.
Feature interactions are non-linear and heterogeneous.
You need a baseline that is easy to inspect and debug.

Implementation workflow

Prepare cleaned tabular features and split train/validation data.
Fit the classifier and inspect depth, splits, and leaf purity.
Tune depth/min-sample settings to reduce overfitting.

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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FAQ

What problem does DecisionTreeClassifier solve in JavaScript machine learning projects?

DecisionTreeClassifier helps teams implement production-ready ML workflows in browser and Node.js environments with a familiar scikit-learn-style API.

When should I choose DecisionTreeClassifier instead of other Tree algorithms?

Use DecisionTreeClassifier when it best matches your data shape, labeling strategy, and runtime constraints. Benchmark against at least one alternative in the same module before finalizing defaults.

Can I run DecisionTreeClassifier in both browser and Node.js with @kanaries/ml?

Yes. @kanaries/ml is designed for JavaScript and TypeScript runtimes across browser applications, server-side Node.js services, and edge-friendly workflows.

DecisionTreeClassifier