@wlearn/lightgbm

LightGBM WASM port for wlearn. Gradient boosting for classification and regression, running in browser and Node.js via WebAssembly.

Installation

npm install @wlearn/lightgbm

Usage

import { LGBModel } from '@wlearn/lightgbm'

// Create and train
const model = await LGBModel.create({
  objective: 'binary',
  learning_rate: 0.05,
  num_leaves: 31,
  numRound: 100
})
model.fit(X, y)

// Predict
const predictions = model.predict(X_test)
const probabilities = model.predictProba(X_test)
const accuracy = model.score(X_test, y_test)

// Save and load
const bundle = model.save()
const loaded = await LGBModel.load(bundle)

// Clean up
model.dispose()

Supported objectives

• binary -- binary classification
• multiclass -- multiclass classification (softmax)
• multiclassova -- multiclass one-vs-all
• cross_entropy -- cross-entropy classification
• regression -- regression (default)

License

MIT (upstream LightGBM is MIT-licensed)