@wlearn/automl

Automated model selection for wlearn. Searches over model families and hyperparameters, runs cross-validation, and optionally builds a Caruana ensemble from top candidates.

Part of wlearn (GitHub, all packages).

Install

npm install @wlearn/automl

Requires at least one model package (e.g. @wlearn/xgboost) to do anything useful.

Quick start

const { autoFit } = require('@wlearn/automl')
const { LinearModel } = require('@wlearn/liblinear')
const { XGBModel } = require('@wlearn/xgboost')

const models = [
  ['linear', LinearModel, { task: 'classification' }],
  ['xgb', XGBModel, { task: 'classification' }]
]

const result = await autoFit(models, X, y, {
  scoring: 'accuracy',
  cv: 5,
  nIter: 20,
  ensemble: true,
  ensembleSize: 10,
  refit: true
})

result.model         // best fitted estimator (or ensemble)
result.leaderboard   // ranked candidate results
result.bestScore     // best CV score
result.bestModelName // e.g. 'xgb'
result.bestParams    // winning hyperparameters

result.model.dispose()

Search strategies

• RandomSearch -- random hyperparameter sampling (default)
• SuccessiveHalvingSearch -- early stopping with increasing resource allocation
• PortfolioSearch -- predefined portfolio of known-good configurations
• ProgressiveSearch -- progressive resource allocation

Each model provides a default search space via Model.defaultSearchSpace(). AutoML samples from these automatically.

Portfolio

The portfolio contains pre-tuned hyperparameter configs for 15 model families:

| Model | Configs | Package | |-------|---------|---------| | xgb | 10 | @wlearn/xgboost | | lgb | 6 | @wlearn/lightgbm | | ebm | 4 | @wlearn/ebm | | linear | 4 | @wlearn/liblinear | | svm | 4 | @wlearn/libsvm | | knn | 3 | @wlearn/nanoflann | | tsetlin | 3 | @wlearn/tsetlin | | rf | 4 | @wlearn/rf | | mlp | 3 | @wlearn/nn (MLPClassifier/Regressor) | | tabm | 3 | @wlearn/nn (TabMClassifier/Regressor) | | nam | 3 | @wlearn/nn (NAMClassifier/Regressor) | | gam | 4 | @wlearn/gam | | bart | 3 | @wlearn/stochtree | | fm | 2 | @wlearn/xlearn (FM) | | xlr | 2 | @wlearn/xlearn (LR) |

Classification and regression have separate config sets with task-appropriate parameters.

autoFit options

• scoring -- metric name ('accuracy', 'r2', 'neg_mse') or custom function
• cv -- number of CV folds (default: 5)
• nIter -- number of random search iterations (default: 10)
• seed -- random seed for reproducibility
• task -- 'classification' or 'regression' (auto-detected if omitted)
• ensemble -- build Caruana ensemble from top candidates (default: false)
• ensembleSize -- max ensemble members (default: 20)
• refit -- refit best model on full data (default: true)

Leaderboard

result.leaderboard is an array of CandidateResult objects sorted by score:

{
  id: 0,
  modelName: 'xgb',
  params: { max_depth: 6, eta: 0.1, ... },
  scores: Float64Array([0.92, 0.94, 0.91, 0.93, 0.90]),
  meanScore: 0.92,
  stdScore: 0.014,
  fitTimeMs: 42,
  rank: 1
}

License

Apache-2.0