Confusion Chart

class datarobot.models.confusion_chart.ConfusionChart(source, data, source_model_id)

Confusion Chart data for model.

Notes

ClassMetrics is a dict containing the following:

  • class_name (string) name of the class

  • actual_count (int) number of times this class is seen in the validation data

  • predicted_count (int) number of times this class has been predicted for the validation data

  • f1 (float) F1 score

  • recall (float) recall score

  • precision (float) precision score

  • was_actual_percentages (list of dict) one vs all actual percentages in format specified below.

    • other_class_name (string) the name of the other class

    • percentage (float) the percentage of the times this class was predicted when is was actually class (from 0 to 1)

  • was_predicted_percentages (list of dict) one vs all predicted percentages in format specified below.

    • other_class_name (string) the name of the other class

    • percentage (float) the percentage of the times this class was actual predicted (from 0 to 1)

  • confusion_matrix_one_vs_all (list of list) 2d list representing 2x2 one vs all matrix.

    • This represents the True/False Negative/Positive rates as integer for each class. The data structure looks like:

    • [ [ True Negative, False Positive ], [ False Negative, True Positive ] ]

Attributes:
sourcestr

Confusion Chart data source. Can be ‘validation’, ‘crossValidation’ or ‘holdout’.

raw_datadict

All of the raw data for the Confusion Chart

confusion_matrixlist of list

The N x N confusion matrix

classeslist

The names of each of the classes

class_metricslist of dicts

List of dicts with schema described as ClassMetrics above.

source_model_idstr

ID of the model this Confusion chart represents; in some cases, insights from the parent of a frozen model may be used