regressor
) - Fitted scikit-learn regressor objectndarray
) - Training data matrixndarray
) - Testing data matrixndarray
) - The regression target for trainingndarray
) -The regression target for testingint
, optional, default is 1000) - Log first nrows
rows of test predictions.bool
, optional, default is True
) - If True
, calculate and log chart visualizations. log_learning_curve_chart,
log_feature_importance_chart
, log_residuals_chart
, log_prediction_error_chart
, log_cooks_distance_chart
functions from this module.dict
with all metadata, that can be assigned to the run namespace. run["summary"] = create_regressor_summary(...)
classifier
) - Fitted scikit-learn classifier objectndarray
) - Training data matrixndarray
) - Testing data matrixndarray
) - The classification target for trainingndarray
) -The classification target for testingint
, optional, default is 1000) - Log first nrows
rows of test predictions and predictions probabilities.bool
, optional, default is True
) - If True
, calculate and log chart visualizations. log_classification_report_chart
, log_confusion_matrix_chart
, log_roc_auc_chart
, log_precision_recall_chart
, log_class_prediction_error_chart
functions from this module.dict
with all metadata, that can be assigned to the run namespace. run["summary"] = create_classifier_summary(...)
KMeans
) - KMeans objectndarray
) - Training instances to clusterint
, optional, default is 1000) - Number of rows to log in the cluster labels.dict
with all metadata, that can be assigned to the run namespace. run["summary"] = create_kmeans_summary(...)
estimator
) - Scikit-learn estimator from which to log parametersdict
with all parameters mapped to their values.estimator
) - Scikit-learn estimator to pickle.File
value object with a pickled model that you can log to the run.y_pred
, then predictions are not computed from X_test
data. estimator
) - Scikit-learn estimator to compute predictions.ndarray
) - Testing data matrixndarray
) - The regression target for testingndarray
, optional, default is None
) - Estimator predictions on test data.int
, optional, default is 1000) - Number of rows to log.File
value object with test predictions as a table that you can log to the run.X_test
, then predictions probabilities are computed from data. y_pred_proba
, then predictions probabilities are not computed from X_test
data. classifier
) - Scikit-learn classifier to compute predictions probabilities.ndarray
) - Testing data matrixndarray
, optional, default is None
) - Classifier predictions probabilities on test data.int
, optional, default is 1000) - Number of rows to log.File
value object with test prediction probabilities as a table that you can log to the run.X
.y_pred
, then predictions are not computed from X
and y
data.estimator
) - Scikit-learn estimator to compute scores.ndarray
) - Data matrix.ndarray
) - Target for testing.ndarray
, optional, default is None
) - Estimator predictions on data.dict
with scores.regressor
) - Fitted scikit-learn regressor objectndarray
) - Training data matrixndarray
) - The regression target for training.File
value object with a learning curve chart that you can log to the run.regressor
) - Fitted scikit-learn regressor objectndarray
) - Training data matrixndarray
) - The regression target for training.File
value object with a feature importance chart that you can log to the run.regressor
) - Fitted scikit-learn regressor object.ndarray
) - Training data matrix.ndarray
) - Testing data matrix.ndarray
) - The regression target for training.ndarray
) - The regression target for testing.File
value object with a residuals chart that you can log to the run.regressor
) - Fitted scikit-learn regressor object.ndarray
) - Training data matrix.ndarray
) - Testing data matrix.ndarray
) - The regression target for training.ndarray
) - The regression target for testing.File
value object with a prediction error chart that you can log to the run.regressor
) - Fitted scikit-learn regressor object.ndarray
) - Training data matrix.ndarray
) - The regression target for training.File
value object with a cooks distance chart that you can log to the run.classifier
) - Fitted scikit-learn regressor object.ndarray
) - Training data matrix.ndarray
) - Testing data matrix.ndarray
) - The classification target for training.ndarray
) - The classification target for testing.File
value object with a classification report chart that you can log to the run.