evidently.metric_preset
Bases:
MetricPreset
Metrics preset for classification performance.
Contains metrics:
- ClassificationQualityMetric
- ClassificationClassBalance
- ClassificationConfusionMatrix
- ClassificationQualityByClass
columns : Optional[List[str]]
k : Optional[int]
probas_threshold : Optional[float]
Bases:
MetricPreset
Metric Preset for Data Drift analysis.
Contains metrics:
- DatasetDriftMetric
- DataDriftTable
cat_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]
cat_stattest_threshold : Optional[float]
columns : Optional[List[str]]
drift_share : float
num_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]
num_stattest_threshold : Optional[float]
per_column_stattest : Optional[Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]]
per_column_stattest_threshold : Optional[Dict[str, float]]
stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]
stattest_threshold : Optional[float]
Bases:
MetricPreset
Metric preset for Data Quality analysis.
Contains metrics:
- DatasetSummaryMetric
- ColumnSummaryMetric for each column
- DatasetMissingValuesMetric
- DatasetCorrelationsMetric
- Parameters
columns
– list of columns for analysis.
columns : Optional[List[str]]
Bases:
MetricPreset
Metric preset for Regression performance analysis.
Contains metrics:
- RegressionQualityMetric
- RegressionPredictedVsActualScatter
- RegressionPredictedVsActualPlot
- RegressionErrorPlot
- RegressionAbsPercentageErrorPlot
- RegressionErrorDistribution
- RegressionErrorNormality
- RegressionTopErrorMetric
- RegressionErrorBiasTable
columns : Optional[List[str]]
class TargetDriftPreset(columns: Optional[List[str]] = None, stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, cat_stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, num_stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, per_column_stattest: Optional[Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]] = None, stattest_threshold: Optional[float] = None, cat_stattest_threshold: Optional[float] = None, num_stattest_threshold: Optional[float] = None, per_column_stattest_threshold: Optional[Dict[str, float]] = None)
Bases:
MetricPreset
Metric preset for Target Drift analysis.
Contains metrics:
- ColumnDriftMetric - for target and prediction if present in datasets.
- ColumnValuePlot - if task is regression.
- ColumnCorrelationsMetric - for target and prediction if present in datasets.
- TargetByFeaturesTable
cat_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]
cat_stattest_threshold : Optional[float]
columns : Optional[List[str]]
num_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]
num_stattest_threshold : Optional[float]
per_column_stattest : Optional[Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]]
per_column_stattest_threshold : Optional[Dict[str, float]]
stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]
stattest_threshold : Optional[float]
Bases:
MetricPreset
Metrics preset for classification performance.
Contains metrics:
- ClassificationQualityMetric
- ClassificationClassBalance
- ClassificationConfusionMatrix
- ClassificationQualityByClass
columns : Optional[List[str]]
k : Optional[int]
probas_threshold : Optional[float]
Bases:
MetricPreset
Metric Preset for Data Drift analysis.
Contains metrics:
- DatasetDriftMetric
- DataDriftTable
cat_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]
cat_stattest_threshold : Optional[float]
columns : Optional[List[str]]
drift_share : float
num_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]
num_stattest_threshold : Optional[float]
per_column_stattest : Optional[Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]]
per_column_stattest_threshold : Optional[Dict[str, float]]
stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]
stattest_threshold : Optional[float]
Bases:
MetricPreset
Metric preset for Data Quality analysis.
Contains metrics:
- DatasetSummaryMetric
- ColumnSummaryMetric for each column
- DatasetMissingValuesMetric
- DatasetCorrelationsMetric
- Parameters
columns
– list of columns for analysis.
columns : Optional[List[str]]
Bases:
object
Base class for metric presets
Bases:
MetricPreset
Metric preset for Regression performance analysis.
Contains metrics:
- RegressionQualityMetric
- RegressionPredictedVsActualScatter
- RegressionPredictedVsActualPlot
- RegressionErrorPlot
- RegressionAbsPercentageErrorPlot
- RegressionErrorDistribution
- RegressionErrorNormality
- RegressionTopErrorMetric
- RegressionErrorBiasTable
columns : Optional[List[str]]
class TargetDriftPreset(columns: Optional[List[str]] = None, stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, cat_stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, num_stattest: Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]] = None, per_column_stattest: Optional[Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]] = None, stattest_threshold: Optional[float] = None, cat_stattest_threshold: Optional[float] = None, num_stattest_threshold: Optional[float] = None, per_column_stattest_threshold: Optional[Dict[str, float]] = None)
Bases:
MetricPreset
Metric preset for Target Drift analysis.
Contains metrics:
- ColumnDriftMetric - for target and prediction if present in datasets.
- ColumnValuePlot - if task is regression.
- ColumnCorrelationsMetric - for target and prediction if present in datasets.
- TargetByFeaturesTable
cat_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]
cat_stattest_threshold : Optional[float]
columns : Optional[List[str]]
num_stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]
num_stattest_threshold : Optional[float]
per_column_stattest : Optional[Dict[str, Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]]
per_column_stattest_threshold : Optional[Dict[str, float]]
stattest : Optional[Union[str, Callable[[Series, Series, str, float], Tuple[float, bool]], StatTest]]
stattest_threshold : Optional[float]
Last modified 10mo ago