ClassificationMetricResults

class ClassificationMetricResults(true_negative_rate, true_positive_rate, positive_predictive_value, negative_predictive_value, false_negative_rate, false_positive_rate, false_discovery_rate, false_omission_rate, positive_likelihood_ratio, negative_likelihood_ratio, prevalence_threshold, threat_score, fowlkes_mallows_index, informedness, markedness, diagnostic_odds_ratio, roc_auc_score, accuracy_score, balanced_accuracy_score, f1_score, average_precision_score, matthews_correlation_coefficient)

Bases: abc.ClassificationMetricResultsBase

Results from computing metrics.

Methods Summary

from_dict(kvs, *[, infer_missing])	rtype ~A
from_json(s, *[, parse_float, parse_int, ...])	rtype ~A
from_scores(y_true, y_score)	Return an instance of these metrics from a given set of true and scores.
get_metric(name)	Get the given metric from the results.
schema(*[, infer_missing, only, exclude, ...])	rtype SchemaF[~A]
to_dict([encode_json])	rtype Dict[str, Union[dict, list, str, int, float, bool, None]]
to_json(*[, skipkeys, ensure_ascii, ...])	rtype str

Methods Documentation

classmethod from_dict(kvs, *, infer_missing=False)

Return type

~A

classmethod from_json(s, *, parse_float=None, parse_int=None, parse_constant=None, infer_missing=False, **kw)

Return type

~A

classmethod from_scores(y_true, y_score)

Return an instance of these metrics from a given set of true and scores.

get_metric(name)

Get the given metric from the results.

Parameters

name (str) – The name of the metric

Return type

float

Returns

The value for the metric

classmethod schema(*, infer_missing=False, only=None, exclude=(), many=False, context=None, load_only=(), dump_only=(), partial=False, unknown=None)

Return type

SchemaF[~A]

to_dict(encode_json=False)

Return type

Dict[str, Union[dict, list, str, int, float, bool, None]]

to_json(*, skipkeys=False, ensure_ascii=True, check_circular=True, allow_nan=True, indent=None, separators=None, default=None, sort_keys=False, **kw)

Return type

str