Table of Contents

Mojo function

GridSearchCV

fn def GridSearchCV[m_type: CV](X: Matrix, y: Matrix, param_grid: Dict[String, List[String]], scoring: def(Matrix, Matrix) raises thin -> Float32, neg_score: Bool = False, n_jobs: Int = Int(0), cv: Int = Int(5)) -> Tuple[Dict[String, String], Float32]

Exhaustive search over specified parameter values for an estimator.

Parameters:

  • m_type (CV): Model type.

Args:

  • X (Matrix): Samples.
  • y (Matrix): Targets.
  • param_grid (Dict[String, List[String]]): Dictionary with parameters names as keys and lists of parameter settings to try as values.
  • scoring (def(Matrix, Matrix) raises thin -> Float32): Scoring function.
  • neg_score (Bool): Invert the scoring results when finding the best params.
  • n_jobs (Int): Number of jobs to run in parallel. -1 means using all processors.
  • cv (Int): Number of folds in a KFold.

Returns:

Tuple[Dict[String, String], Float32]: Best parameters.

Raises:

Edit this page