Class: TunedThresholdClassifierCV
Classifier that post-tunes the decision threshold using cross-validation.
This estimator post-tunes the decision threshold (cut-off point) that is 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. The tuning is done by optimizing a binary metric, potentially constrained by a another metric.
Read more in the User Guide.
Constructors
new TunedThresholdClassifierCV()
new TunedThresholdClassifierCV(
opts?):TunedThresholdClassifierCV
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.cv? | number | "prefit" | Determines the cross-validation splitting strategy to train classifier. Possible inputs for cv are: |
opts.estimator? | any | The classifier, fitted or not, for which we want to optimize the decision threshold used during predict. |
opts.n_jobs? | number | The number of jobs to run in parallel. When cv represents a cross-validation strategy, the fitting and scoring on each data split is done in parallel. undefined means 1 unless in a joblib.parallel_backend context. \-1 means using all processors. See Glossary for more details. |
opts.random_state? | number | Controls the randomness of cross-validation when cv is a float. See Glossary. |
opts.refit? | boolean | Whether or not to refit the classifier on the entire training set once the decision threshold has been found. Note that forcing refit=False on cross-validation having more than a single split will raise an error. Similarly, refit=True in conjunction with cv="prefit" will raise an error. |
opts.response_method? | "auto" | "predict_proba" | "decision_function" | Methods by the classifier estimator corresponding to the decision function for which we want to find a threshold. It can be: |
opts.scoring? | string | The objective metric to be optimized. Can be one of: |
opts.store_cv_results? | boolean | Whether to store all scores and thresholds computed during the cross-validation process. |
opts.thresholds? | number | ArrayLike | The number of decision threshold to use when discretizing the output of the classifier method. Pass an array-like to manually specify the thresholds to use. |
Returns TunedThresholdClassifierCV
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:25
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/model_selection/TunedThresholdClassifierCV.ts:23 |
_isInitialized | boolean | false | generated/model_selection/TunedThresholdClassifierCV.ts:22 |
_py | PythonBridge | undefined | generated/model_selection/TunedThresholdClassifierCV.ts:21 |
id | string | undefined | generated/model_selection/TunedThresholdClassifierCV.ts:18 |
opts | any | undefined | generated/model_selection/TunedThresholdClassifierCV.ts:19 |
Accessors
best_score_
Get Signature
get best_score_():
Promise<number>
The optimal score of the objective metric, evaluated at best_threshold_.
Returns Promise<number>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:528
best_threshold_
Get Signature
get best_threshold_():
Promise<number>
The new decision threshold.
Returns Promise<number>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:501
cv_results_
Get Signature
get cv_results_():
Promise<any>
A dictionary containing the scores and thresholds computed during the cross-validation process. Only exist if store_cv_results=True. The keys are "thresholds" and "scores".
Returns Promise<any>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:555
estimator_
Get Signature
get estimator_():
Promise<any>
The fitted classifier used when predicting.
Returns Promise<any>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:474
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.
Returns Promise<ArrayLike>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:609
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.
Returns Promise<number>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:582
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:85
Methods
decision_function()
decision_function(
opts):Promise<ArrayLike>
Decision function for samples in X using the fitted estimator.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training vectors, where n_samples is the number of samples and n_features is the number of features. |
Returns Promise<ArrayLike>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:158
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:141
fit()
fit(
opts):Promise<any>
Fit the classifier.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.params? | any | Parameters to pass to the fit method of the underlying classifier. |
opts.X? | ArrayLike | Training data. |
opts.y? | ArrayLike | Target values. |
Returns Promise<any>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:194
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRouter encapsulating routing information. |
Returns Promise<any>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:242
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:98
predict()
predict(
opts):Promise<ArrayLike>
Predict the target of new samples.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The samples, as accepted by estimator.predict. |
Returns Promise<ArrayLike>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:278
predict_log_proba()
predict_log_proba(
opts):Promise<ArrayLike[]>
Predict logarithm class probabilities for X using the fitted estimator.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training vectors, where n_samples is the number of samples and n_features is the number of features. |
Returns Promise<ArrayLike[]>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:314
predict_proba()
predict_proba(
opts):Promise<ArrayLike[]>
Predict class probabilities for X using the fitted estimator.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training vectors, where n_samples is the number of samples and n_features is the number of features. |
Returns Promise<ArrayLike[]>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:350
score()
score(
opts):Promise<number>
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
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.X? | ArrayLike[] | Test samples. |
opts.y? | ArrayLike | True labels for X. |
Returns Promise<number>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:388
set_score_request()
set_score_request(
opts):Promise<any>
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:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample_weight parameter in score. |
Returns Promise<any>
Defined in generated/model_selection/TunedThresholdClassifierCV.ts:438