class sklearn_evaluation.ClassifierEvaluator(estimator=None, y_true=None, y_pred=None, y_score=None, feature_names=None, target_names=None, estimator_name=None, X=None)

Encapsuates results from an estimator on a testing set to provide a simplified API from other modules. All parameters are optional, just fill the ones you need for your analysis.

  • estimator (sklearn estimator) – Must have a feature_importances_ attribute.

  • y_true (array-like) – Target predicted classes (estimator predictions).

  • y_pred (array-like) – Correct target values (ground truth).

  • y_score (array-like) – Target scores (estimador predictions).

  • feature_names (array-like) – Feature names.

  • target_names (list) – List containing the names of the target classes

  • estimator_name (str) – Identifier for the model. This can be later used to idenfity the estimator when generaing reports.


Confusion matrix plot

property estimator_class

Estimator class (e.g. sklearn.ensemble.RandomForestClassifier)

property estimator_type

Estimator name (e.g. RandomForestClassifier)


Feature importances plot


Feature importances table


Returns a EvaluatorHTMLSerializer instance, which is an object with the same methods and properties than a ClassifierEvaluator, but it returns HTML serialized versions of each (i.e. evaluator.feature_importances_table() returns a string with the table in HTML format, evaluator.confusion_matrix() returns a HTML image element with the image content encoded in base64), useful for generating reports using some template system


Make HTML report

  • template (str, or pathlib.Path, optional) – HTML or Markdown template with jinja2 format. If a pathlib.Path object is passed, the content of the file is read. Within the template, the evaluator is passed as “e”, so you can use things like {{e.confusion_matrix()}} or any other attribute/method. If None, a default template is used

  • style (str) – Path to a css file to apply style to the report. If None, no style will be applied


Returns the contents of the report if path is None.

Return type



Precision at proportions plot


Precision-recall plot


ROC plot