Tree

• DecisionTreeClassifier

• ExtraTreeClassifier

• DecisionTreeRegressor

• ExtraTreeRegressor

How to use the Tree module in real projects

The Tree module provides interpretable decision rules for classification and regression on heterogeneous tabular features.

Selection checklist

1. Use DecisionTree variants when model explainability and rule extraction matter.
2. Use ExtraTree variants when you want stronger randomization and variance reduction.
3. Control depth, split criteria, and validation performance to avoid overfitting.

Common implementation workflow

1. Start from a simple baseline in this module and evaluate on a holdout split.
2. Compare at least one alternative algorithm from this module before locking production defaults.
3. Pair model quality metrics with runtime constraints (latency, memory, bundle size).

Common search intents

• decision tree javascript
• extra tree classifier typescript
• interpretable ml nodejs

Explore algorithms in this module

• decisionTreeClassifier
• decisionTreeRegressor
• extraTreeClassifier
• extraTreeRegressor

FAQ

What problem does Tree solve in JavaScript machine learning projects?

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

When should I choose Tree instead of other Tree algorithms?

Use Tree 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 Tree 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.