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

Tree.DecisionTreeRegressor

interface RegressionTreeProps {
    max_depth?: number;
    min_samples_split?: number;
}

constructor(props: RegressionTreeProps = {})

Example

const reg = new DecisionTreeRegressor({ max_depth: 5 });
reg.fit(X, Y);
const preds = reg.predict(T);

Practical guide: DecisionTreeRegressor in JavaScript and TypeScript

DecisionTreeRegressor models non-linear numeric targets with interpretable split rules.

When to use DecisionTreeRegressor

Linear regression misses stepwise or interaction-driven behavior.
You need explainable regression predictions for stakeholders.
Fast inference with simple control over model complexity is required.

Implementation workflow

Train with baseline depth constraints and evaluate residuals.
Inspect important splits for domain plausibility.
Tune split constraints to balance bias and variance.

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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DecisionTreeRegressor JavaScript
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@kanaries/ml DecisionTreeRegressor

FAQ

What problem does DecisionTreeRegressor solve in JavaScript machine learning projects?

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

When should I choose DecisionTreeRegressor instead of other Tree algorithms?

Use DecisionTreeRegressor 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 DecisionTreeRegressor 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.

DecisionTreeRegressor