Metrics in H2O

H2O Model Metrics

Regression model.

copyright:

3. 2016 H2O.ai

license:

Apache License Version 2.0 (see LICENSE for details)

class h2o.model.metrics_base.H2OAnomalyDetectionModelMetrics(metric_json, on=None, algo='')

Bases: h2o.model.metrics_base.MetricsBase

mean_normalized_score()

Mean Normalized Anomaly Score. For Isolation Forest - normalized average path length.

mean_score()

Mean Anomaly Score. For Isolation Forest represents the average of all tree-path lengths.

class h2o.model.metrics_base.H2OAutoEncoderModelMetrics(metric_json, on=None, algo='')

Bases: h2o.model.metrics_base.MetricsBase

class h2o.model.metrics_base.H2OBinomialModelMetrics(metric_json, on=None, algo='')

Bases: h2o.model.metrics_base.MetricsBase

This class is essentially an API for the AUC object. This class contains methods for inspecting the AUC for different criteria. To input the different criteria, use the static variable criteria.

F0point5(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:The F0.5 for this set of metrics and thresholds.

F1(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:The F1 for the given set of thresholds.

F2(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:The F2 for this set of metrics and thresholds.

accuracy(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:The accuracy for this set of metrics and thresholds.

confusion_matrix(metrics=None, thresholds=None)

Get the confusion matrix for the specified metric

Parameters:

• metrics – A string (or list of strings) among metrics listed in maximizing_metrics. Defaults to ‘f1’.
• thresholds – A value (or list of values) between 0 and 1. If None, then the thresholds maximizing each provided metric will be used.

Returns:

a list of ConfusionMatrix objects (if there are more than one to return), or a single ConfusionMatrix (if there is only one).

error(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold minimizing the error will be used. Returns:The error for this set of metrics and thresholds.

fallout(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:The fallout (same as False Positive Rate) for this set of metrics and thresholds.

find_idx_by_threshold(threshold)

Retrieve the index in this metric’s threshold list at which the given threshold is located.

Parameters:threshold – Find the index of this input threshold. Returns:the index Raises:ValueError – if no such index can be found.

find_threshold_by_max_metric(metric)

Parameters:metrics – A string among the metrics listed in maximizing_metrics. Returns:the threshold at which the given metric is maximal.

fnr(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:The False Negative Rate.

fpr(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:The False Positive Rate.

fprs

Return all false positive rates for all threshold values.

Returns:a list of false positive rates.

gains_lift()

Retrieve the Gains/Lift table.

max_per_class_error(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold minimizing the error will be used. Returns:Return 1 - min(per class accuracy).

maximizing_metrics = ('absolute_mcc', 'accuracy', 'precision', 'f0point5', 'f1', 'f2', 'mean_per_class_accuracy', 'min_per_class_accuracy', 'tns', 'fns', 'fps', 'tps', 'tnr', 'fnr', 'fpr', 'tpr', 'sensitivity', 'specificity', 'recall', 'fallout', 'missrate')

metrics names allowed for confusion matrix

mcc(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:The absolute MCC (a value between 0 and 1, 0 being totally dissimilar, 1 being identical).

mean_per_class_error(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold minimizing the error will be used. Returns:mean per class error.

metric(metric, thresholds=None)

Parameters:

• metric (str) – A metric among maximizing_metrics.
• thresholds – thresholds parameter must be a number or a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. If ‘all’, then all stored thresholds are used and returned with the matching metric.

Returns:

The set of metrics for the list of thresholds. The returned list has a ‘value’ property holding only the metric value (if no threshold provided or if provided as a number), or all the metric values (if thresholds provided as a list)

metrics_aliases = {'fallout': 'fpr', 'missrate': 'fnr', 'recall': 'tpr', 'sensitivity': 'tpr', 'specificity': 'tnr'}

missrate(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:The miss rate (same as False Negative Rate).

plot(type='roc', server=False)

Produce the desired metric plot.

Parameters:

• type – the type of metric plot (currently, only ROC supported).
• server – if True, generate plot inline using matplotlib’s “Agg” backend.

Returns:

None

precision(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:The precision for this set of metrics and thresholds.

recall(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:Recall for this set of metrics and thresholds.

roc()

Return the coordinates of the ROC curve as a tuple containing the false positive rates as a list and true positive rates as a list. :returns: The ROC values.

sensitivity(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:Sensitivity or True Positive Rate for this set of metrics and thresholds.

specificity(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:The specificity (same as True Negative Rate).

tnr(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:The True Negative Rate.

tpr(thresholds=None)

Parameters:thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used. Returns:The True Postive Rate.

tprs

Return all true positive rates for all threshold values.

Returns:a list of true positive rates.

class h2o.model.metrics_base.H2OClusteringModelMetrics(metric_json, on=None, algo='')

Bases: h2o.model.metrics_base.MetricsBase

betweenss()

The Between Cluster Sum-of-Square Error, or None if not present.

tot_withinss()

The Total Within Cluster Sum-of-Square Error, or None if not present.

totss()

The Total Sum-of-Square Error to Grand Mean, or None if not present.

class h2o.model.metrics_base.H2OCoxPHModelMetrics(metric_json, on=None, algo='')

Bases: h2o.model.metrics_base.MetricsBase

class h2o.model.metrics_base.H2ODimReductionModelMetrics(metric_json, on=None, algo='')

Bases: h2o.model.metrics_base.MetricsBase

cat_err()

The Number of Misclassified categories over non-missing categorical entries, or None if not present.

num_err()

Sum of Squared Error over non-missing numeric entries, or None if not present.

class h2o.model.metrics_base.H2OHGLMModelMetrics(metric_json, on=None, algo='HGLM Gaussian Gaussian')

Bases: h2o.model.metrics_base.MetricsBase

class h2o.model.metrics_base.H2OMultinomialModelMetrics(metric_json, on=None, algo='')

Bases: h2o.model.metrics_base.MetricsBase

confusion_matrix()

Returns a confusion matrix based of H2O’s default prediction threshold for a dataset.

hit_ratio_table()

Retrieve the Hit Ratios.

class h2o.model.metrics_base.H2OOrdinalModelMetrics(metric_json, on=None, algo='')

Bases: h2o.model.metrics_base.MetricsBase

confusion_matrix()

Returns a confusion matrix based of H2O’s default prediction threshold for a dataset.

hit_ratio_table()

Retrieve the Hit Ratios.

class h2o.model.metrics_base.H2ORegressionModelMetrics(metric_json, on=None, algo='')

Bases: h2o.model.metrics_base.MetricsBase

This class provides an API for inspecting the metrics returned by a regression model.

It is possible to retrieve the R^2 (1 - MSE/variance) and MSE.

class h2o.model.metrics_base.H2OTargetEncoderMetrics(metric_json, on=None, algo='')

Bases: h2o.model.metrics_base.MetricsBase

class h2o.model.metrics_base.H2OWordEmbeddingModelMetrics(metric_json, on=None, algo='')

Bases: h2o.model.metrics_base.MetricsBase

class h2o.model.metrics_base.List

Bases: list

class h2o.model.metrics_base.MetricsBase(*args, **kwargs)

Bases: h2o.model.metrics_base.MetricsBase

A parent class to house common metrics available for the various Metrics types.

The methods here are available across different model categories.

aic()

The AIC for this set of metrics.

auc()

The AUC for this set of metrics.

aucpr()

The area under the precision recall curve.

custom_metric_name()

Name of custom metric or None.

custom_metric_value()

Value of custom metric or None.

gini()

Gini coefficient.

hglm_metric(metric_string)

logloss()

Log loss.

mae()

The MAE for this set of metrics.

classmethod make(kvs)

Factory method to instantiate a MetricsBase object from the list of key-value pairs.

mean_per_class_error()

The mean per class error.

mean_residual_deviance()

The mean residual deviance for this set of metrics.

mse()

The MSE for this set of metrics.

nobs()

The number of observations.

null_degrees_of_freedom()

The null DoF if the model has residual deviance, otherwise None.

null_deviance()

The null deviance if the model has residual deviance, otherwise None.

pr_auc()

MetricsBase.pr_auc is deprecated, please use MetricsBase.aucpr instead.

r2()

The R squared coefficient.

residual_degrees_of_freedom()

The residual DoF if the model has residual deviance, otherwise None.

residual_deviance()

The residual deviance if the model has it, otherwise None.

rmse()

The RMSE for this set of metrics.

rmsle()

The RMSLE for this set of metrics.

show()

Display a short summary of the metrics.

H2O Grid Metrics

class h2o.grid.metrics.H2OAutoEncoderGridSearch

Bases: object

anomaly(test_data, per_feature=False)

Obtain the reconstruction error for the input test_data.

Parameters:

• test_data (H2OFrame) – The dataset upon which the reconstruction error is computed.
• per_feature (bool) – Whether to return the square reconstruction error per feature. Otherwise, return the mean square error.

Returns:

the reconstruction error.

class h2o.grid.metrics.H2OBinomialGridSearch

Bases: object

F0point5(thresholds=None, train=False, valid=False, xval=False)

Get the F0.5 for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the F0point5 value for the training data.
• valid (bool) – If valid is True, then return the F0point5 value for the validation data.
• xval (bool) – If xval is True, then return the F0point5 value for the cross validation data.

Returns:

The F0point5 for this binomial model.

F1(thresholds=None, train=False, valid=False, xval=False)

Get the F1 values for a set of thresholds for the models explored.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If True, return the F1 value for the training data.
• valid (bool) – If True, return the F1 value for the validation data.
• xval (bool) – If True, return the F1 value for each of the cross-validated splits.

Returns:

Dictionary of model keys to F1 values

F2(thresholds=None, train=False, valid=False, xval=False)

Get the F2 for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the F2 value for the training data.
• valid (bool) – If valid is True, then return the F2 value for the validation data.
• xval (bool) – If xval is True, then return the F2 value for the cross validation data.

Returns:

Dictionary of model keys to F2 values.

accuracy(thresholds=None, train=False, valid=False, xval=False)

Get the accuracy for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the accuracy value for the training data.
• valid (bool) – If valid is True, then return the accuracy value for the validation data.
• xval (bool) – If xval is True, then return the accuracy value for the cross validation data.

Returns:

The accuracy for this binomial model.

confusion_matrix(metrics=None, thresholds=None, train=False, valid=False, xval=False)

Get the confusion matrix for the specified metrics/thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• metrics – A string (or list of strings) among metrics listed in H2OBinomialModelMetrics.maximizing_metrics. Defaults to ‘f1’.
• thresholds – A value (or list of values) between 0 and 1. If None, then the thresholds maximizing each provided metric will be used.
• train (bool) – If train is True, then return the confusion matrix value for the training data.
• valid (bool) – If valid is True, then return the confusion matrix value for the validation data.
• xval (bool) – If xval is True, then return the confusion matrix value for the cross validation data.

Returns:

The confusion matrix for this binomial model.

error(thresholds=None, train=False, valid=False, xval=False)

Get the error for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold minimizing the error will be used.
• train (bool) – If train is True, then return the error value for the training data.
• valid (bool) – If valid is True, then return the error value for the validation data.
• xval (bool) – If xval is True, then return the error value for the cross validation data.

Returns:

The error for this binomial model.

fallout(thresholds=None, train=False, valid=False, xval=False)

Get the Fallout (AKA False Positive Rate) for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the fallout value for the training data.
• valid (bool) – If valid is True, then return the fallout value for the validation data.
• xval (bool) – If xval is True, then return the fallout value for the cross validation data.

Returns:

The fallout for this binomial model.

find_idx_by_threshold(threshold, train=False, valid=False, xval=False)

Retrieve the index in this metric’s threshold list at which the given threshold is located.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• threshold (float) – The threshold value to search for.
• train (bool) – If train is True, then return the idx_by_threshold for the training data.
• valid (bool) – If valid is True, then return the idx_by_threshold for the validation data.
• xval (bool) – If xval is True, then return the idx_by_threshold for the cross validation data.

Returns:

The idx_by_threshold for this binomial model.

find_threshold_by_max_metric(metric, train=False, valid=False, xval=False)

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• metric (str) – A metric among the metrics listed in H2OBinomialModelMetrics.maximizing_metrics.
• train (bool) – If train is True, then return the threshold_by_max_metric value for the training data.
• valid (bool) – If valid is True, then return the threshold_by_max_metric value for the validation data.
• xval (bool) – If xval is True, then return the threshold_by_max_metric value for the cross validation data.

Returns:

The threshold_by_max_metric for this binomial model.

fnr(thresholds=None, train=False, valid=False, xval=False)

Get the False Negative Rates for a set of thresholds. If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the FNR value for the training data.
• valid (bool) – If valid is True, then return the FNR value for the validation data.
• xval (bool) – If xval is True, then return the FNR value for the cross validation data.

Returns:

The FNR for this binomial model.

fpr(thresholds=None, train=False, valid=False, xval=False)

Get the False Positive Rates for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the FPR value for the training data.
• valid (bool) – If valid is True, then return the FPR value for the validation data.
• xval (bool) – If xval is True, then return the FPR value for the cross validation data.

Returns:

The FPR for this binomial model.

max_per_class_error(thresholds=None, train=False, valid=False, xval=False)

Get the max per class error for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold minimizing the error will be used.
• train (bool) – If train is True, then return the max_per_class_error value for the training data.
• valid (bool) – If valid is True, then return the max_per_class_error value for the validation data.
• xval (bool) – If xval is True, then return the max_per_class_error value for the cross validation data.

Returns:

The max per class error for this binomial model.

mcc(thresholds=None, train=False, valid=False, xval=False)

Get the MCC for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the mcc value for the training data.
• valid (bool) – If valid is True, then return the mcc value for the validation data.
• xval (bool) – If xval is True, then return the mcc value for the cross validation data.

Returns:

The MCC for this binomial model.

mean_per_class_error(thresholds=None, train=False, valid=False, xval=False)

Get the mean per class error for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold minimizing the error will be used.
• train (bool) – If train is True, then return the mean_per_class_error value for the training data.
• valid (bool) – If valid is True, then return the mean_per_class_error value for the validation data.
• xval (bool) – If xval is True, then return the mean_per_class_error value for the cross validation data.

Returns:

The mean per class error for this binomial model.

metric(metric, thresholds=None, train=False, valid=False, xval=False)

Get the metric value for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• metric – name of the metric to compute.
• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the metrics for the training data.
• valid (bool) – If valid is True, then return the metrics for the validation data.
• xval (bool) – If xval is True, then return the metrics for the cross validation data.

Returns:

The metrics for this binomial model.

missrate(thresholds=None, train=False, valid=False, xval=False)

Get the miss rate (AKA False Negative Rate) for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the missrate value for the training data.
• valid (bool) – If valid is True, then return the missrate value for the validation data.
• xval (bool) – If xval is True, then return the missrate value for the cross validation data.

Returns:

The missrate for this binomial model.

precision(thresholds=None, train=False, valid=False, xval=False)

Get the precision for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the precision value for the training data.
• valid (bool) – If valid is True, then return the precision value for the validation data.
• xval (bool) – If xval is True, then return the precision value for the cross validation data.

Returns:

The precision for this binomial model.

recall(thresholds=None, train=False, valid=False, xval=False)

Get the Recall (AKA True Positive Rate) for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the recall value for the training data.
• valid (bool) – If valid is True, then return the recall value for the validation data.
• xval (bool) – If xval is True, then return the recall value for the cross validation data.

Returns:

The recall for this binomial model.

roc(train=False, valid=False, xval=False)

Return the coordinates of the ROC curve for a given set of data, as a two-tuple containing the false positive rates as a list and true positive rates as a list.

If all are False (default), then return the training data. If more than one ROC curve is requested, the data is returned as a dictionary of two-tuples.

Parameters:

• train (bool) – If train is true, then return the ROC coordinates for the training data.
• valid (bool) – If valid is true, then return the ROC coordinates for the validation data.
• xval (bool) – If xval is true, then return the ROC coordinates for the cross validation data.

Returns:

the true cooridinates of the roc curve.

sensitivity(thresholds=None, train=False, valid=False, xval=False)

Get the sensitivity (AKA True Positive Rate or Recall) for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the sensitivity value for the training data.
• valid (bool) – If valid is True, then return the sensitivity value for the validation data.
• xval (bool) – If xval is True, then return the sensitivity value for the cross validation data.

Returns:

The sensitivity for this binomial model.

specificity(thresholds=None, train=False, valid=False, xval=False)

Get the specificity (AKA True Negative Rate) for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the specificity value for the training data.
• valid (bool) – If valid is True, then return the specificity value for the validation data.
• xval (bool) – If xval is True, then return the specificity value for the cross validation data.

Returns:

The specificity for this binomial model.

tnr(thresholds=None, train=False, valid=False, xval=False)

Get the True Negative Rate for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the TNR value for the training data.
• valid (bool) – If valid is True, then return the TNR value for the validation data.
• xval (bool) – If xval is True, then return the TNR value for the cross validation data.

Returns:

The TNR for this binomial model.

tpr(thresholds=None, train=False, valid=False, xval=False)

Get the True Positive Rate for a set of thresholds.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• thresholds – thresholds parameter must be a list (i.e. [0.01, 0.5, 0.99]). If None, then the threshold maximizing the metric will be used.
• train (bool) – If train is True, then return the TPR value for the training data.
• valid (bool) – If valid is True, then return the TPR value for the validation data.
• xval (bool) – If xval is True, then return the TPR value for the cross validation data.

Returns:

The TPR for this binomial model.

class h2o.grid.metrics.H2OClusteringGridSearch

Bases: object

betweenss(train=False, valid=False, xval=False)

Get the between cluster sum of squares.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If True, then return the between cluster sum of squares value for the training data.
• valid (bool) – If True, then return the between cluster sum of squares value for the validation data.
• xval (bool) – If True, then return the between cluster sum of squares value for each of the cross-validated splits.

Returns:

the between cluster sum of squares values for the specified key(s).

centers()

Returns the centers for the KMeans model.

centers_std()

Returns the standardized centers for the kmeans model.

centroid_stats(train=False, valid=False, xval=False)

Get the centroid statistics for each cluster.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If True, then return the centroid statistics for the training data.
• valid (bool) – If True, then return the centroid statistics for the validation data.
• xval (bool) – If True, then return the centroid statistics for each of the cross-validated splits.

Returns:

the centroid statistics for the specified key(s).

num_iterations()

Get the number of iterations that it took to converge or reach max iterations.

size(train=False, valid=False, xval=False)

Get the sizes of each cluster.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If True, then return the cluster sizes for the training data.
• valid (bool) – If True, then return the cluster sizes for the validation data.
• xval (bool) – If True, then return the cluster sizes for each of the cross-validated splits.

Returns:

the cluster sizes for the specified key(s).

tot_withinss(train=False, valid=False, xval=False)

Get the total within cluster sum of squares.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If True, then return the total within cluster sum of squares for the training data.
• valid (bool) – If True, then return the total within cluster sum of squares for the validation data.
• xval (bool) – If True, then return the total within cluster sum of squares for each of the cross-validated splits.

Returns:

the total within cluster sum of squares values for the specified key(s).

totss(train=False, valid=False, xval=False)

Get the total sum of squares.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If True, then return total sum of squares for the training data.
• valid (bool) – If True, then return the total sum of squares for the validation data.
• xval (bool) – If True, then return the total sum of squares for each of the cross-validated splits.

Returns:

the total sum of squares values for the specified key(s).

withinss(train=False, valid=False, xval=False)

Get the within cluster sum of squares for each cluster.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If True, then return within cluster sum of squares for the training data.
• valid (bool) – If True, then return the within cluster sum of squares for the validation data.
• xval (bool) – If True, then return the within cluster sum of squares for each of the cross-validated splits.

Returns:

the within cluster sum of squares values for the specified key(s).

class h2o.grid.metrics.H2ODimReductionGridSearch

Bases: object

archetypes()

Returns:the archetypes (Y) of the GLRM model.

final_step()

Get the final step size from the GLRM model.

Returns:final step size (double)

num_iterations()

Get the number of iterations that it took to converge or reach max iterations.

Returns:number of iterations (integer)

objective()

Get the final value of the objective function from the GLRM model.

Returns:final objective value (double)

class h2o.grid.metrics.H2OMultinomialGridSearch

Bases: object

confusion_matrix(data)

Returns a confusion matrix based of H2O’s default prediction threshold for a dataset.

Parameters:data – metric for which the confusion matrix will be calculated.

hit_ratio_table(train=False, valid=False, xval=False)

Retrieve the Hit Ratios.

If all are False (default), then return the training metric value. If more than one option is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If train is True, then return the hit ratio value for the training data.
• valid (bool) – If valid is True, then return the hit ratio value for the validation data.
• xval (bool) – If xval is True, then return the hit ratio value for the cross validation data.

Returns:

The hit ratio for this multinomial model.

mean_per_class_error(train=False, valid=False, xval=False)

Get the mean per class error.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If train is True, then return the mean per class error value for the training data.
• valid (bool) – If valid is True, then return the mean per class error value for the validation data.
• xval (bool) – If xval is True, then return the mean per class error value for the cross validation data.

Returns:

The mean per class error for this multinomial model.

class h2o.grid.metrics.H2OOrdinalGridSearch

Bases: object

confusion_matrix(data)

Returns a confusion matrix based of H2O’s default prediction threshold for a dataset.

Parameters:data – metric for which the confusion matrix will be calculated.

hit_ratio_table(train=False, valid=False, xval=False)

Retrieve the Hit Ratios.

If all are False (default), then return the training metric value. If more than one option is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If train is True, then return the hit ratio value for the training data.
• valid (bool) – If valid is True, then return the hit ratio value for the validation data.
• xval (bool) – If xval is True, then return the hit ratio value for the cross validation data.

Returns:

The hit ratio for this ordinal model.

mean_per_class_error(train=False, valid=False, xval=False)

Get the mean per class error.

If all are False (default), then return the training metric value. If more than one options is set to True, then return a dictionary of metrics where the keys are “train”, “valid”, and “xval”.

Parameters:

• train (bool) – If train is True, then return the mean per class error value for the training data.
• valid (bool) – If valid is True, then return the mean per class error value for the validation data.
• xval (bool) – If xval is True, then return the mean per class error value for the cross validation data.

Returns:

The mean per class error for this ordinal model.

class h2o.grid.metrics.H2ORegressionGridSearch

Bases: object