model_evaluation#

model_evaluation(alternative_scorers: Optional[Dict[str, Callable]] = None, columns: Optional[Union[Hashable, List[Hashable]]] = None, ignore_columns: Optional[Union[Hashable, List[Hashable]]] = None, n_top_columns: Optional[int] = None, n_samples: Optional[int] = None, random_state: int = 42, n_to_show: int = 5, **kwargs) Suite[source]#

Suite for evaluating the model’s performance over different metrics, segments, error analysis, examining overfitting, comparing to baseline, and more.

List of Checks:
List of Checks#

Check Example

API Reference

ROC Report

RocReport

Confusion Matrix Report

ConfusionMatrixReport

Segment Performance

SegmentPerformance

Train Test Prediction Drift

TrainTestPredictionDrift

Simple Model Comparison

SimpleModelComparison

Calibration Score

CalibrationScore

Regression Systematic Error

RegressionSystematicError

Regression Error Distribution

RegressionErrorDistribution

Unused Features

UnusedFeatures

Boosting Overfit

BoostingOverfit

Model Inference Time

ModelInferenceTime

Parameters
alternative_scorersDict[str, Callable], default: None

An optional dictionary of scorer name to scorer functions. If none given, use default scorers

columnsUnion[Hashable, List[Hashable]] , default: None

The columns to be checked. If None, all columns will be checked except the ones in ignore_columns.

ignore_columnsUnion[Hashable, List[Hashable]] , default: None

The columns to be ignored. If None, no columns will be ignored.

n_top_columnsint , optional

number of columns to show ordered by feature importance (date, index, label are first) (check dependent)

n_samplesint , default: 1_000_000

number of samples to use for checks that sample data. If none, use the default n_samples per check.

random_stateint, default: 42

random seed for all checks.

n_to_showint , default: 5

number of top results to show (check dependent)

**kwargsdict

additional arguments to pass to the checks.

Returns
Suite

A suite for evaluating the model’s performance.

Examples

>>> from deepchecks.tabular.suites import model_evaluation
>>> suite = model_evaluation(columns=['a', 'b', 'c'], n_samples=1_000_000)
>>> result = suite.run()
>>> result.show()
run(self, train_dataset: Optional[Union[Dataset, DataFrame]] = None, test_dataset: Optional[Union[Dataset, DataFrame]] = None, model: Optional[BasicModel] = None, feature_importance: Optional[Series] = None, feature_importance_force_permutation: bool = False, feature_importance_timeout: int = 120, with_display: bool = True, y_pred_train: Optional[ndarray] = None, y_pred_test: Optional[ndarray] = None, y_proba_train: Optional[ndarray] = None, y_proba_test: Optional[ndarray] = None) SuiteResult#

Run all checks.

Parameters
train_dataset: Optional[Union[Dataset, pd.DataFrame]] , default None

object, representing data an estimator was fitted on

test_datasetOptional[Union[Dataset, pd.DataFrame]] , default None

object, representing data an estimator predicts on

modelOptional[BasicModel] , default None

A scikit-learn-compatible fitted estimator instance

feature_importance: pd.Series , default: None

pass manual features importance

feature_importance_force_permutationbool , default: False

force calculation of permutation features importance

feature_importance_timeoutint , default: 120

timeout in second for the permutation features importance calculation

y_pred_train: Optional[np.ndarray] , default: None

Array of the model prediction over the train dataset.

y_pred_test: Optional[np.ndarray] , default: None

Array of the model prediction over the test dataset.

y_proba_train: Optional[np.ndarray] , default: None

Array of the model prediction probabilities over the train dataset.

y_proba_test: Optional[np.ndarray] , default: None

Array of the model prediction probabilities over the test dataset.

Returns
SuiteResult

All results by all initialized checks