| Name | Type | Description | Notes |
|---|---|---|---|
| total | int | total number of features for this analysis | [optional] |
| total_correct | int | total number of correct predictions for this analysis | [optional] |
| accuracy | float | accuracy of the predictions, defined as #correct/ #features | [optional] |
| total_reviewed | int | Number of features audited (given the Audit threshold) | [optional] |
| review_volume | float | The proportion of all features that are reviewed | [optional] |
| incorrect_predicted_below_threshold | int | The number of misclassified features that are reviewed | [optional] |
| incorrect_predicted | int | The number of misclassified features that are reviewed | [optional] |
| audit_success_rate | float | The proportion of misclassified features that are reviewed (scale 0-1, 1 is good) | [optional] |
| classes | \Swagger\Client\Model\MachineLearningPerformanceEntity | [optional] | |
| confusion_matrix | object | A Hash for the confusion matrix. consists of (e.g) for a true / false classification {actual_false:{predicted_true: x, predicted_false: y}, actual_true:{predicted_true: z, predicted_false:c}} | [optional] |
| confusion_matrix_below_threshold | object | A Hash for the confusion matrix for items getting audited. consists of (e.g) for a true / false classification {actual_false:{predicted_true: x, predicted_false: y}, actual_true:{predicted_true: z, predicted_false:c}} | [optional] |