TabularPredictor.explain_classification_errors#
- TabularPredictor.explain_classification_errors(data, model=None, print_rules: bool = True)[source]#
Explain classification errors by fitting a rule-based model to them
- Parameters
data (str or
TabularDataset
orpd.DataFrame
) – The data to make predictions for. Should contain same column names as training Dataset and follow same format (may contain extra columns that won’t be used by Predictor, including the label-column itself). If str is passed, data will be loaded using the str value as the file path.model (str (optional)) – The name of the model to get predictions from. Defaults to None, which uses the highest scoring model on the validation set. Valid models are listed in this predictor by calling predictor.get_model_names()
print_rules (bool, optional) – Whether to print the learned rules
- Returns
cls – Interpretable rule-based classifier with fit/predict methods
- Return type
imodels.classifier