evidently.metrics.classification_performance
k : Optional[Union[float, int]]
probas_threshold : Optional[float]
render_html(obj: ClassificationClassBalance)
render_json(obj: ClassificationClassBalance)
Bases:
object
plot_data : Dict[str, int]
render_html(obj: ClassificationClassSeparationPlot)
render_json(obj: ClassificationClassSeparationPlot)
Bases:
object
current_plot : Optional[DataFrame] = None
reference_plot : Optional[DataFrame] = None
target_name : str
Bases:
ThresholdClassificationMetric
[ClassificationDummyMetricResults
] quality_metric : ClassificationQualityMetric
correction_for_threshold(dummy_results: DatasetClassificationQuality, threshold: float, target: Series, labels: list, probas_shape: tuple)
render_html(obj: ClassificationDummyMetric)
render_json(obj: ClassificationDummyMetric)
class ClassificationDummyMetricResults(dummy: DatasetClassificationQuality, by_reference_dummy: Optional[DatasetClassificationQuality], model_quality: Optional[DatasetClassificationQuality], metrics_matrix: dict)
Bases:
object
metrics_matrix : dict
Bases:
ThresholdClassificationMetric
[ClassificationQualityMetricResult
] confusion_matrix_metric : ClassificationConfusionMatrix
render_html(obj: ClassificationQualityMetric)
render_json(obj: ClassificationQualityMetric)
class ClassificationQualityMetricResult(current: DatasetClassificationQuality, reference: Optional[DatasetClassificationQuality], target_name: str)
Bases:
object
target_name : str
Bases:
ThresholdClassificationMetric
[ClassificationConfusionMatrixResult
] k : Optional[Union[float, int]]
probas_threshold : Optional[float]
render_html(obj: ClassificationConfusionMatrix)
render_json(obj: ClassificationConfusionMatrix)
class ClassificationConfusionMatrixResult(current_matrix: ConfusionMatrix, reference_matrix: Optional[ConfusionMatrix])
Bases:
object
render_html(obj: ClassificationPRCurve)
render_json(obj: ClassificationPRCurve)
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object
current_pr_curve : Optional[dict] = None
reference_pr_curve : Optional[dict] = None
render_html(obj: ClassificationPRTable)
render_json(obj: ClassificationPRTable)
Bases:
object
current_pr_table : Optional[dict] = None
reference_pr_table : Optional[dict] = None
static get_distribution(dataset: DataFrame, target_name: str, prediction_labels: Iterable)
render_html(obj: ClassificationProbDistribution)
render_json(obj: ClassificationProbDistribution)
Bases:
object
current_distribution : Optional[Dict[str, list]]
reference_distribution : Optional[Dict[str, list]]
Bases:
ThresholdClassificationMetric
[ClassificationQualityByClassResult
] k : Optional[Union[float, int]]
probas_threshold : Optional[float]
render_html(obj: ClassificationQualityByClass)
render_json(obj: ClassificationQualityByClass)
class ClassificationQualityByClassResult(columns: DatasetColumns, current_metrics: dict, current_roc_aucs: Optional[list], reference_metrics: Optional[dict], reference_roc_aucs: Optional[dict])
Bases:
object
current_metrics : dict
current_roc_aucs : Optional[list]
reference_metrics : Optional[dict]
reference_roc_aucs : Optional[dict]
columns : Optional[List[str]]
render_html(obj: ClassificationQualityByFeatureTable)
render_json(obj: ClassificationQualityByFeatureTable)
class ClassificationQualityByFeatureTableResults(current_plot_data: pandas.core.frame.DataFrame, reference_plot_data: Optional[pandas.core.frame.DataFrame], target_name: str, curr_predictions: PredictionData, ref_predictions: Optional[PredictionData], columns: List[str])
Bases:
object
columns : List[str]
current_plot_data : DataFrame
reference_plot_data : Optional[DataFrame]
target_name : str
render_html(obj: ClassificationRocCurve)
render_json(obj: ClassificationRocCurve)
Bases:
object
current_roc_curve : Optional[dict] = None
reference_roc_curve : Optional[dict] = None
Last modified 6mo ago