Skip to main content
For an intro, read Core Concepts and check quickstarts for LLMs or ML. For a reference code example, see this Metric cookbook.
  • Metric: the name of Metric or Preset you can pass to Report.
  • Description: what it does. Complex Metrics link to explainer pages.
  • Parameters: available options. You can also add conditional tests to any Metric with standard operators like eq (equal), gt (greater than), etc. How Tests work.
  • Test defaults are conditions that apply when you invoke Tests but do not set a pass/fail condition yourself.
    • With reference: if you provide a reference dataset during the Report run, the conditions are set relative to reference.
    • No reference: if you do not provide a reference, Tests will use fixed heuristics (like expect no missing values).

​
Text Evals

Summarizes results of text or LLM evals. To score individual inputs, first use descriptors.
Data definition. You may need to map text columns.
MetricDescriptionParametersTest Defaults
TextEvals()Optional:
  • columns
As in Metrics included in ValueStats

​
Columns

Use to aggregate descriptor results or check data quality on column level.
You may need to map column types using Data definition.

​
Value stats

Descriptive statistics.
MetricDescriptionParametersTest Defaults
ValueStats()
  • Small Preset, column-level.
  • Computes various descriptive stats. Included Metrics: UniqueValueCount, MissingValueCount, MinValue, MaxValue, MeanValue, StdValue, QuantileValue (0.25, 0.5, 0.75).
  • Returns different stats based on the column type.
Required:
  • column
Optional:
  • No reference. As in individual Metrics.
  • With reference. As in indiviudal Metrics.
MinValue()
  • Column-level.
  • Returns min value for a given numerical column.
  • Metric result: value.
Required:
  • column
Optional:
  • No reference. N/A.
  • With reference. Fails if Min Value is differs by more than 10% (+/-).
StdValue()
  • Column-level.
  • Computes the standard deviation of a given numerical column.
  • Metric result: value.
Required:
  • column
Optional:
  • No reference. N/A.
  • With reference. Fails if the standard deviation differs by more than 10% (+/-).
MeanValue()
  • Column-level.
  • Computes the mean value of a given numerical column.
  • Metric result: value.
Required:
  • column
Optional:
  • No reference. N/A.
  • With reference. Fails if the mean value differs by more than 10%.
MaxValue()
  • Column-level.
  • Computes the max value of a given numerical column.
  • Metric result: value.
Required:
  • column
Optional:
  • No reference. N/A.
  • With reference. Fails if the max value is higher than in the reference.
MedianValue()
  • Column-level.
  • Computes the median value of a given numerical column.
  • Metric result: value.
Required:
  • column
Optional:
  • No reference. N/A.
  • With reference. Fails if the median value differs by more than 10% (+/-).
QuantileValue()
  • Column-level.
  • Computes the quantile value of a given numerical column.
  • Defaults to 0.5 if no quantile is specified.
  • Metric result: value.
Required:
  • column
Optional:
  • No reference. N/A.
  • With reference. Fails if quantile value differs by more than 10% (+/-).
CategoryCount()

Example:
CategoryCount(
column="city",
category="NY")
  • Column-level.
  • Counts occurrences of the specified category or categories.
  • To check the joint share of several categories, pass the list categories=["a", "b"].
  • Metric result: count, share.
Required:
  • column
  • category
  • categories
Optional:
  • No reference. N/A.
  • With reference. Fails if the specified category is not present.

​
Column data quality

Column-level data quality metrics.
Data definition. You may need to map column types.
MetricDescriptionParametersTest Defaults
MissingValueCount()
  • Column-level.
  • Counts the number and share of missing values.
  • Metric result: count, share.
Required:
  • column
Optional:
  • No reference: Fails if there are missing values.
  • With reference: Fails if share of missing values is >10% higher.
InRangeValueCount()

Example:
InRangeValueCount(
column="age",
left="1", right="18")
  • Column-level.
  • Counts the number and share of values in the set range.
  • Metric result: count, share.
Required:
  • column
  • left
  • right
Optional:
  • No reference: N/A.
  • With reference: Fails if column contains values out of the min-max reference range.
OutRangeValueCount()
  • Column-level.
  • Counts the number and share of values out of the set range.
  • Metric result: count, share.
Required:
  • column
  • left
  • right
Optional:
  • No reference: N/A.
  • With reference: Fails if any value is out of min-max reference range.
InListValueCount()
  • Column-level.
  • Counts the number and share of values in the set list.
  • Metric result: count, share.
Required:
  • column
  • values
Optional:
  • No reference: N/A.
  • With reference: Fails if any value is out of list.
OutListValueCount()

Example:
OutListValueCount(
column="city",
values=["Lon", "NY"])
  • Column-level.
  • Counts the number and share of values out of the set list.
  • Metric result: count, share.
Required:
  • column
  • values
Optional:
  • No reference: N/A.
  • With reference: Fails if any value is out of list.
UniqueValueCount()
  • Column-level.
  • Counts the number and share of unique values.
  • Metric result: values (dict with count, share).
Required:
  • column
Optional:
  • No reference: N/A.
  • With reference: Fails if the share of unique values differs by >10% (+/-).

​
Dataset

Use for exploratory data analysis and data quality checks.
Data definition. You may need to map column types, ID and timestamp.

​
Dataset stats

Descriptive statistics.
MetricDescriptionParametersTest Defaults
DataSummaryPreset()
  • Large Preset.
  • Combines DatasetStats and ValueStats for all or specified columns.
  • Metric result: for all Metrics.
  • Preset page
Optional:
  • columns
As in individual Metrics.
DatasetStats()
  • Small preset.
  • Dataset-level.
  • Calculates descriptive dataset stats, including columns by type, rows, missing values, empty columns, etc.
  • Metric result: for all Metrics.
None
  • No reference: As in included Metrics
  • With reference: As in included Metrics.
RowCount()
  • Dataset-level.
  • Counts the number of rows.
  • Metric result: value.
Optional:
  • No reference: N/A.
  • With reference: Fails if row count differs by >10%.
ColumnCount()
  • Dataset-level.
  • Counts the number of columns.
  • Metric result: value.
Optional:
  • No reference: N/A.
  • With reference: Fails if not equal to reference.

​
Dataset data quality

Dataset-level data quality metrics.
Data definition. You may need to map column types, ID and timestamp.
MetricDescriptionParametersTest Defaults
ConstantColumnsCount()
  • Dataset-level.
  • Counts the number of constant columns.
  • Metric result: value.
Optional:
  • No reference: Fails if there is at least one constant column.
  • With reference: Fails if count is higher than in reference.
EmptyRowsCount()
  • Dataset-level.
  • Counts the number of empty rows.
  • Metric result: value.
Optional:
  • No reference: Fails if there is at least one empty row.
  • With reference: Fails if share differs by >10%.
EmptyColumnsCount()
  • Dataset-level.
  • Counts the number of empty columns.
  • Metric result: value.
Optional:
  • No reference: Fails if there is at least one empty column.
  • With reference: Fails if count is higher than in reference.
DuplicatedRowCount()
  • Dataset-level.
  • Counts the number of duplicated rows.
  • Metric result: value.
Optional:
  • No reference: Fails if there is at least one duplicated row.
  • With reference: Fails if share differs by >10% (+/-).
DuplicatedColumnsCount()
  • Dataset-level.
  • Counts the number of duplicated columns.
  • Metric result: value.
Optional:
  • No reference: Fails if there is at least one duplicated column.
  • With reference: Fails if count is higher than in reference.
DatasetMissingValueCount()
  • Dataset-level.
  • Calculates the number and share of missing values.
  • Displays the number of missing values per column.
  • Metric result: share, count.
Required:
  • columns
Optional:
  • No reference: Fails if there are missing values.
  • With reference: Fails if share is >10% higher than reference (+/-).
AlmostConstantColumnsCount()
  • Dataset-level.
  • Counts almost constant columns (95% identical values).
  • Metric result: value.
Optional:
  • No reference: Fails if there is at least one almost constant column.
  • With reference: Fails if count is higher than in reference.
ColumnsWithMissingValuesCount()
  • Dataset-level.
  • Counts columns with missing values.
  • Metric result: value.
Optional:
  • No reference: Fails if there is at least one column with missing values.
  • With reference: Fails if count is higher than in reference.

​
Data Drift

Use to detect distribution drift for text and tabular data or over computed text descriptors. Checks 20+ drift methods listed separately: text and tabular.
Data definition. You may need to map column types, ID and timestamp.
Metrics explainers. Understand how data drift works.
MetricDescriptionParametersTest Defaults
DataDriftPreset()
  • Large Preset.
  • Requires reference.
  • Calculates data drift for all or set columns.
  • Uses the default or set method.
  • Returns drift score for each column.
  • Visualizes all distributions.
  • Metric result: all Metrics.
  • Preset page.
Optional:
  • columns
  • method
  • cat_method
  • num_method
  • per_column_method
  • threshold
  • cat_threshold
  • num_threshold
  • per_column_threshold
See drift options.
  • With reference: Data drift defaults, depending on column type. See drift methods.
DriftedColumnsCount()
  • Dataset-level.
  • Requires reference.
  • Calculates the number and share of drifted columns in the dataset.
  • Each column is tested for drift using the default algorithm or set method.
  • Returns only the total number of drifted columns.
  • Metric result: count, share.
Optional:
  • columns
  • method
  • cat_method
  • num_method
  • per_column_method
  • threshold
  • cat_threshold
  • num_threshold
  • per_column_threshold
See drift options.
  • With reference: Fails if 50% of columns are drifted.
ValueDrift()
  • Column-level.
  • Requires reference.
  • Calculates data drift for a defined column (num, cat, text).
  • Visualizes distributions.
  • Metric result: value.
Required:
  • column
Optional:
  • method
  • threshold
See drift options.
  • With reference: Data drift defaults, depending on column type. See drift methods.

​
Classification

Use to evaluate quality on a classification task (probabilistic, non-probabilistic, binary and multi-class).
Data definition. You may need to map prediction, target columns and classification type.

​
General

Use for binary classification and aggregated results for multi-class.
MetricDescriptionParametersTest Defaults
ClassificationPreset()
  • Large Preset with many classification Metrics and visuals.
  • See Preset page.
  • Metric result: all Metrics.
Optional: probas_threshold .As in individual Metrics.
ClassificationQuality()
  • Small Preset.
  • Summarizes quality Metrics in a single widget.
  • Metric result: all Metrics.
Optional: probas_thresholdAs in individual Metrics.
Accuracy()
  • Calculates accuracy.
  • Metric result: value.
Optional:
  • No reference: Fails if lower than dummy model accuracy.
  • With reference: Fails if accuracy differs by >20%.
Precision()
  • Calculates precision.
  • Visualizations available: Confusion Matrix, PR Curve, PR Table.
  • Metric result: value.
Required:
  • Set at least one visualization: conf_matrix, pr_curve, pr_table.
Optional:
  • probas_threshold (default: None or 0.5 for probabilistic classification)
  • top_k
  • Test conditions
  • No reference: Fails if Precision is lower than the dummy model.
  • With reference: Fails if Precision differs by >20%.
Recall()
  • Calculates recall.
  • Visualizations available: Confusion Matrix, PR Curve, PR Table.
  • Metric result: value.
Required:
  • Set at least one visualization: conf_matrix, pr_curve, pr_table.
Optional:
  • No reference: Fails if lower than dummy model recall.
  • With reference: Fails if Recall differs by >20%.
F1Score()
  • Calculates F1 Score.
  • Metric result: value.
Required:
  • Set at least one visualization: conf_matrix.
Optional:
  • No reference: Fails if lower than dummy model F1.
  • With reference: Fails if F1 differs by >20%.
TPR()
  • Calculates True Positive Rate (TPR).
  • Metric result: value.
Required:
  • Set at least one visualization: pr_table.
Optional:
  • No reference: Fails if TPR is lower than the dummy model.
  • With reference: Fails if TPR differs by >20%.
TNR()
  • Calculates True Negative Rate (TNR).
  • Metric result: value.
Required:
  • Set at least one visualization: pr_table.
Optional:
  • No reference: Fails if TNR is lower than the dummy model.
  • With reference: Fails if TNR differs by >20%.
FPR()
  • Calculates False Positive Rate (FPR).
  • Metric result: value.
Required:
  • Set at least one visualization: pr_table.
Optional:
  • No reference: Fails if FPR is higher than the dummy model.
  • With reference: Fails if FPR differs by >20%.
FNR()
  • Calculates False Negative Rate (FNR).
  • Metric result: value.
Required:
  • Set at least one visualization: pr_table.
Optional:
  • No reference: Fails if FNR is higher than the dummy model.
  • With reference: Fails if FNR differs by >20%.
LogLoss()
  • Calculates Log Loss.
  • Metric result: value.
Required:
  • Set at least one visualization: pr_table.
Optional:
  • No reference: Fails if LogLoss is higher than the dummy model (equals 0.5 for a constant model).
  • With reference: Fails if LogLoss differs by >20%.
RocAUC()
  • Calculates ROC AUC.
  • Can visualize PR curve or table.
  • Metric result: value.
Required:
  • Set at least one visualization: pr_table, roc_curve.
Optional:
  • No reference: Fails if ROC AUC is ≤ 0.5.
  • With reference: Fails if ROC AUC differs by >20%.
Dummy metrics:
Use these Metics to get the quality of a dummy model created on the same data (based on heuristics). You can compare your model quality to verify that it’s better than random. These Metrics serve as a baseline in automated testing.
MetricDescriptionParametersTest Defaults
ClassificationDummyQuality()
  • Small Preset summarizing quality of a dummy model.
  • Metric result: all Metrics
N/AN/A
DummyPrecision()
  • Calculates precision for a dummy model.
  • Metric result: value.
N/AN/A
DummyRecall()
  • Calculates recall for a dummy model.
  • Metric result: value.
N/AN/A
DummyF1()
  • Calculates F1 Score for a dummy model.
  • Metric result: value.
N/AN/A

​
By label

Use when you have multiple classes and want to evaluate quality separately.
MetricDescriptionParametersTest Defaults
ClassificationQualityByLabel()
  • Small Preset summarizing classification quality Metrics by label.
  • Metric result: all Metrics.
NoneAs in individual Metrics.
PrecisionByLabel()
  • Calculates precision by label in multiclass classification.
  • Metric result (dict): label: value.
Optional:
  • No reference: Fails if Precision is lower than the dummy model.
  • With reference: Fails if Precision differs by >20%.
F1ByLabel()
  • Calculates F1 Score by label in multiclass classification.
  • Metric result (dict): label: value.
Optional:
  • No reference: Fails if F1 is lower than the dummy model.
  • With reference: Fails if F1 differs by >20%.
RecallByLabel()
  • Calculates recall by label in multiclass classification.
  • Metric result (dict): label: value
Optional:
  • No reference: Fails if Recall is lower than the dummy model.
  • With reference: Fails if Recall differs by >20%.
RocAUCByLabel()
  • Calculates ROC AUC by label in multiclass classification.
  • Metric result (dict): label: value
Optional:
  • No reference: Fails if ROC AUC is ≤ 0.5.
  • With reference: Fails if ROC AUC differs by >20%.

​
Regression

Use to evaluate the quality of a regression model.
Data definition. You may need to map prediction and target columns.
MetricDescriptionParametersTest Defaults
RegressionPreset
  • Large Preset.
  • Includes a wide range of regression metrics with rich visuals.
  • Metric result: all metrics.
  • See Preset page.
None.As in individual metrics.
RegressionQuality
  • Small Preset.
  • Summarizes key regression metrics in a single widget.
  • Metric result: all metrics.
None.As in individual metrics.
MeanError()
  • Calculates the mean error.
  • Visualizations available: Error Plot, Error Distribution, Error Normality.
  • Metric result: mean, std.
Required:
  • Set at least one visualization: error_plot, error_distr, error_normality.
Optional:
  • No reference/With reference: Expect ME to be near zero. Fails if Mean Error is skewed and condition is violated: eq = approx(absolute=0.1 * error_std).
MAE()
  • Calculates Mean Absolute Error (MAE).
  • Visualizations available: Error Plot, Error Distribution, Error Normality.
  • Metric result: mean, std.
Required:
  • Set at least one visualization: error_plot, error_distr, error_normality.
Optional:
  • No reference: Fails if MAE is higher than the dummy model predicting the median target value.
  • With reference: Fails if MAE differs by >10%.
RMSE()
  • Calculates Root Mean Square Error (RMSE).
  • Metric result: value.
Optional:
  • No reference: Fails if RMSE is higher than the dummy model predicting the mean target value.
  • With reference: Fails if RMSE differs by >10%.
MAPE()
  • Calculates Mean Absolute Percentage Error (MAPE).
  • Visualizations available: Percentage Error Plot.
  • Metric result: mean, std.
Required:
  • Set at least one visualization: perc_error_plot.
Optional:
  • No reference: Fails if MAPE is higher than the dummy model predicting the weighted median target value.
  • With reference: Fails if MAPE differs by >10%.
R2Score()
  • Calculates R² (Coefficient of Determination).
  • Metric result: value.
Optional:
  • No reference: Fails if R² ≤ 0.
  • With reference: Fails if R² differs by >10%.
AbsMaxError()
  • Calculates Absolute Maximum Error.
  • Metric result: value.
Optional:
  • No reference: Fails if absolute maximum error is higher than the dummy model predicting the median target value.
  • With reference: Fails if it differs by >10%.
Dummy metrics:
Use these Metics to get the baseline quality for regression: they use optimal constants (varies by the Metric). These Metrics serve as a baseline in automated testing.
MetricDescriptionParametersTest Defaults
RegressionDummyQuality()
  • Small Preset summarizing quality of a dummy model.
  • Metric result: all Metrics
N/AN/A
DummyMeanError()
  • Calculates Mean Error for a dummy model.
  • Metric result: mean_error, error std.
N/AN/A
DummyMAE()
  • Calculates Mean Absolute Error (MAE) for a dummy model.
  • Metric result: mean_absolute_error, absolute_error_std.
N/AN/A
DummyMAPE()
  • Calculates Mean Absolute Percentage Error (MAPE) for a dummy model.
  • Metric result: mean_perc_absolute_error, perc_absolute_error std.
N/AN/A
DummyRMSE()
  • Calculates Root Mean Square Error (RMSE) for a dummy model.
  • Metric result: rmse.
N/AN/A
DummyR2()
  • Calculates Calculates R² (Coefficient of Determination) for a dummy model.
  • Metric result: r2score.
N/AN/A

​
Ranking

Use to evaluate ranking, search / retrieval or recommendations.
Data definition. You may need to map prediction and target columns and ranking type.
Metric explainers. Check ranking metrics explainers.
MetricDescriptionParametersTest Defaults
RecallTopK()
  • Calculates Recall at the top K retrieved items.
  • Metric result: value.
Required:
  • k
Optional:
  • No reference: Tests if recall > 0.
  • With reference: Fails if Recall differs by >10%.
FBetaTopK()
  • Calculates F-beta score at the top K retrieved items.
  • Metric result: value.
Required:
  • k
Optional:
  • No reference: Tests if F-beta > 0.
  • With reference: Fails if F-beta differs by >10%.
PrecisionTopK()
  • Calculates Precision at the top K retrieved items.
  • Metric result: value.
Required:
  • k
Optional:
  • No reference: Tests if Precision > 0.
  • With reference: Fails if Precision differs by >10%.
MAP()
  • Calculates Mean Average Precision at the top K retrieved items.
  • Metric result: value.
Required:
  • k
Optional:
  • No reference: Tests if MAP > 0.
  • With reference: Fails if MAP differs by >10%.
NDCG()
  • Calculates Normalized Discounted Cumulative Gain at the top K retrieved items.
  • Metric result: value.
Required:
  • k
Optional:
  • No reference: Tests if NDCG > 0.
  • With reference: Fails if NDCG differs by >10%.
MRR()
  • Calculates Mean Reciprocal Rank at the top K retrieved items.
  • Metric result: value.
Required:
  • k
Optional:
  • No reference: Tests if MRR > 0.
  • With reference: Fails if MRR differs by >10%.
HitRate()
  • Calculates Hit Rate at the top K retrieved items.
  • Metric result: value.
Required:
  • k
Optional:
  • No reference: Tests if Hit Rate > 0.
  • With reference: Fails if Hit Rate differs by >10%.
ScoreDistribution()
  • Computes the predicted score entropy (KL divergence).
  • Applies only when the recommendations_type is a score..
  • Metric result: value.
Required:
  • k
Optional:
  • No reference:value
  • With reference: value.