watex.utils.evalModel

watex.utils.evalModel(model, X, y, Xt, yt=None, scorer='accuracy', eval=False, **kws)

Evaluate model and quick test the score with metric scorers.

Parameters:

• model (Callable, {'preprocessor + estimator } | estimator,) – the preprocessor is list of step for data handling all encapsulated on the pipeline. model can also be a simple estimator with fit,

• X (N-d array, shape (N, M)) –

  the training set composed of N-columns and the M-samples. The

  feature set excludes the target y.

• y (arraylike , shape (M)) – the target is composed of M-examples in supervised learning.

• Xt (N-d array, shape (N, M)) – test set array composed of N-columns and the M-samples. The feature set excludes the target y.

• yt (arraylike , shape (M)) – test label (or test target) composed of M-examples in supervised learning.

• scorer (str, Callable,) –

  a scorer is a metric function for model evaluation. If given as string it should be the prefix of the following metrics:

  • ”classification_report” -> for classification_report,

  • ’precision_recall’ -> for precision_recall_curve,

  • ”confusion_matrix” -> for a confusion_matrix,

  • ’precision’ -> for precision_score,

  • ”accuracy” -> for accuracy_score

  • ”mse” -> for mean_squared_error,

  • ”recall” -> for recall_score,

  • ’auc’ -> for roc_auc_score,

  • ’roc’ -> for roc_curve

  • ’f1’ -> for f1_score,

  Other string prefix values should raises an errors

• kws (dict,) – Additionnal keywords arguments from scklearn metric function.

Returns:

Tuple – the model score or the predicted y if predict is set to True.

Return type:

(score, ypred)