`sklearn.model_selection`.FixedThresholdClassifier¶

class sklearn.model_selection.FixedThresholdClassifier(estimator, *, threshold='auto', pos_label=None, response_method='auto')[source]¶

Binary classifier that manually sets the decision threshold.

This classifier allows to change the default decision threshold used for converting posterior probability estimates (i.e. output of predict_proba) or decision scores (i.e. output of decision_function) into a class label.

Here, the threshold is not optimized and is set to a constant value.

See also

sklearn.model_selection.TunedThresholdClassifierCV: Classifier that post-tunes the decision threshold based on some metrics and using cross-validation.
sklearn.calibration.CalibratedClassifierCV: Estimator that calibrates probabilities.

Examples

>>> from sklearn.datasets import make_classification
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.metrics import confusion_matrix
>>> from sklearn.model_selection import FixedThresholdClassifier, train_test_split
>>> X, y = make_classification(
...     n_samples=1_000, weights=[0.9, 0.1], class_sep=0.8, random_state=42
... )
>>> X_train, X_test, y_train, y_test = train_test_split(
...     X, y, stratify=y, random_state=42
... )
>>> classifier = LogisticRegression(random_state=0).fit(X_train, y_train)
>>> print(confusion_matrix(y_test, classifier.predict(X_test)))
[[217   7]
 [ 19   7]]
>>> classifier_other_threshold = FixedThresholdClassifier(
...     classifier, threshold=0.1, response_method="predict_proba"
... ).fit(X_train, y_train)
>>> print(confusion_matrix(y_test, classifier_other_threshold.predict(X_test)))
[[184  40]
 [  6  20]]

Methods

`decision_function`(X)	Decision function for samples in `X` using the fitted estimator.
`fit`(X, y, **params)	Fit the classifier.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`predict`(X)	Predict the target of new samples.
`predict_log_proba`(X)	Predict logarithm class probabilities for `X` using the fitted estimator.
`predict_proba`(X)	Predict class probabilities for `X` using the fitted estimator.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_params`(**params)	Set the parameters of this estimator.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.

property classes_¶: Classes labels.

decision_function(X)[source]¶

Decision function for samples in X using the fitted estimator.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Training vectors, where n_samples is the number of samples and n_features is the number of features.

Returns:

decisionsndarray of shape (n_samples,): The decision function computed the fitted estimator.

fit(X, y, **params)[source]¶

Fit the classifier.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Training data.
yarray-like of shape (n_samples,): Target values.
**paramsdict: Parameters to pass to the fit method of the underlying classifier.

Returns:

selfobject: Returns an instance of self.

get_metadata_routing()[source]¶

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:

routingMetadataRouter: A MetadataRouter encapsulating routing information.

get_params(deep=True)[source]¶

Get parameters for this estimator.

Parameters:

deepbool, default=True: If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict: Parameter names mapped to their values.

predict(X)[source]¶

Predict the target of new samples.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): The samples, as accepted by estimator.predict.

Returns:

class_labelsndarray of shape (n_samples,): The predicted class.

predict_log_proba(X)[source]¶

Predict logarithm class probabilities for X using the fitted estimator.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Training vectors, where n_samples is the number of samples and n_features is the number of features.

Returns:

log_probabilitiesndarray of shape (n_samples, n_classes): The logarithm class probabilities of the input samples.

predict_proba(X)[source]¶

Predict class probabilities for X using the fitted estimator.

Parameters:

X{array-like, sparse matrix} of shape (n_samples, n_features): Training vectors, where n_samples is the number of samples and n_features is the number of features.

Returns:

probabilitiesndarray of shape (n_samples, n_classes): The class probabilities of the input samples.

score(X, y, sample_weight=None)[source]¶

Return the mean accuracy on the given test data and labels.

In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.

Parameters:

Xarray-like of shape (n_samples, n_features): Test samples.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True labels for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: Mean accuracy of self.predict(X) w.r.t. y.

set_params(**params)[source]¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**paramsdict: Estimator parameters.

Returns:

selfestimator instance: Estimator instance.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → FixedThresholdClassifier[source]¶

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

Examples using `sklearn.model_selection.FixedThresholdClassifier`¶

Post-tuning the decision threshold for cost-sensitive learning

sklearn.model_selection.FixedThresholdClassifier¶

Examples using sklearn.model_selection.FixedThresholdClassifier¶

`sklearn.model_selection`.FixedThresholdClassifier¶

Examples using `sklearn.model_selection.FixedThresholdClassifier`¶