Options for Quality Metrics
Last updated
Last updated
An example of setting custom options in Data Drift and Probabilistic Classification Performance reports on Wine Quality Dataset:
These options apply to different plots in the Evidently reports: Data Drift, Categorical Target Drift, Numerical Target Drift, Classification Performance, Probabilistic classification performance.
You can specify the following parameters:
conf_interval_n_sigmas: int Default = 1.
Defines the width of confidence interval depicted on plots. Confidence level indicated in sigmas (standard deviation).
Works to the feature or target distribution plots in the Data Drift and Numerical Target Drift reports.
classification_threshold: float. Default = 0.5.
Defines classification threshold for binary probabilistic classification.
Works to the Probabilistic Classification report.
cut_quantile: tuple[str, float] or dict[str, tuple[str, float]. Default = None.
Cut the data above the given quantile from the histogram plot if side parameter == 'right'.
Cut the data below the given quantile from the histogram plot if side parameter == 'left'.
Cut the data below the given quantile and above 1 - the given quantile from the histogram plot if side parameter == 'two-sided'.
Data used for metric calculation doesn't change.
Applies to all features (if passed as tuple) or certain features (if passed as dictionary).
Works to the Categorical Target Drift, Probabilistic Classification and Classification reports, and affects tables with Target/Prediction behavior by feature, and Classification Quality by Feature.
1. Define a QualityMetricsOptions object.
2. Pass it to the Dashboard class:
