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Supervised AutoML

➡️ Pipeline Grid​

All models are implemented directly on top of the library’s Matrix and Vector primitives.

Models can be combined with:

  • Scalers (e.g. StandardScaler)
  • Feature selectors (e.g. SelectKBest)
  • Cross-validation strategies
  • Hyperparameter search grids
var pipelineGrid = new PipelineGrid()
    .AddModel<RandomForest>(g => g
        .Add("NumTrees", 50, 100, 200)
        .Add("MaxDepth", 5, 8, 10))
    .AddModel<Logistic>(g => g
        .Add("LearningRate", 0.05, 0.1)
        .Add("MaxIterations", 1000, 2000)
        .AddScaler<StandardScaler>(s => { })
        .AddSelector<SelectKBest>(s => s
           .Add("K", 1, 2)))
    .AddModel<DecisionTree>(g => g
        .Add("MaxDepth", 3, 5, 8))
    .AddModel<KNearestNeighbors>(g => g
        .Add("K", 3, 5, 7));

🔬 SupervisedExperiment (Fluent API)​

Run a full experiment with grid search and cross-validation in one call:

var result = SupervisedExperiment
    .For(X, y)
    .WithGrid(pipelineGrid)
    .WithCrossValidators(
        CrossValidatorConfig.KFold(folds: 5),
        CrossValidatorConfig.StratifiedKFold(folds: 10),
        CrossValidatorConfig.ShuffleSplit(nSplits: 10, testSize: 0.2))
    .Run();

Key points:

  • For(X, y) takes your feature matrix and target vector
  • WithGrid defines models and hyperparameter combinations — same grid API as PipelineGrid
  • WithCrossValidators accepts one or more cross-validation strategies; the first is the primary used for ranking
  • All combinations of model × hyperparameters × cross-validator are evaluated automatically
  • Results are ranked by the primary cross-validator's mean score