CalibratedClassifierCV
Probability calibration with isotonic regression or logistic regression.
This class uses cross-validation to both estimate the parameters of a classifier and subsequently calibrate a classifier. With default ensemble=True, for each cv split it fits a copy of the base estimator to the training subset, and calibrates it using the testing subset. For prediction, predicted probabilities are averaged across these individual calibrated classifiers. When ensemble=False, cross-validation is used to obtain unbiased predictions, via cross\_val\_predict, which are then used for calibration. For prediction, the base estimator, trained using all the data, is used. This is the method implemented when probabilities=True for sklearn.svm estimators.
Already fitted classifiers can be calibrated via the parameter cv="prefit". In this case, no cross-validation is used and all provided data is used for calibration. The user has to take care manually that data for model fitting and calibration are disjoint.
The calibration is based on the decision_function method of the estimator if it exists, else on predict_proba.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new CalibratedClassifierCV(opts?: object): CalibratedClassifierCV;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.base_estimator? | any | This parameter is deprecated. Use estimator instead. |
opts.cv? | number | "prefit" | Determines the cross-validation splitting strategy. Possible inputs for cv are: |
opts.ensemble? | boolean | Determines how the calibrator is fitted when cv is not 'prefit'. Ignored if cv='prefit'. If true, the estimator is fitted using training data, and calibrated using testing data, for each cv fold. The final estimator is an ensemble of n\_cv fitted classifier and calibrator pairs, where n\_cv is the number of cross-validation folds. The output is the average predicted probabilities of all pairs. If false, cv is used to compute unbiased predictions, via cross\_val\_predict, which are then used for calibration. At prediction time, the classifier used is the estimator trained on all the data. Note that this method is also internally implemented in sklearn.svm estimators with the probabilities=True parameter. Default Value true |
opts.estimator? | any | The classifier whose output need to be calibrated to provide more accurate predict\_proba outputs. The default classifier is a LinearSVC. |
opts.method? | "sigmoid" | "isotonic" | The method to use for calibration. Can be ‘sigmoid’ which corresponds to Platt’s method (i.e. a logistic regression model) or ‘isotonic’ which is a non-parametric approach. It is not advised to use isotonic calibration with too few calibration samples (<<1000) since it tends to overfit. Default Value 'sigmoid' |
opts.n_jobs? | number | Number of jobs to run in parallel. undefined means 1 unless in a joblib.parallel\_backend (opens in a new tab) context. \-1 means using all processors. Base estimator clones are fitted in parallel across cross-validation iterations. Therefore parallelism happens only when cv != "prefit". See Glossary for more details. |
Returns
Defined in: generated/calibration/CalibratedClassifierCV.ts:29 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/calibration/CalibratedClassifierCV.ts:27 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/calibration/CalibratedClassifierCV.ts:26 (opens in a new tab)
_py
PythonBridge
Defined in: generated/calibration/CalibratedClassifierCV.ts:25 (opens in a new tab)
id
string
Defined in: generated/calibration/CalibratedClassifierCV.ts:22 (opens in a new tab)
opts
any
Defined in: generated/calibration/CalibratedClassifierCV.ts:23 (opens in a new tab)
Accessors
calibrated_classifiers_
The list of classifier and calibrator pairs.
Signature
calibrated_classifiers_(): Promise<number>;Returns
Promise<number>
Defined in: generated/calibration/CalibratedClassifierCV.ts:421 (opens in a new tab)
classes_
The class labels.
Signature
classes_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/calibration/CalibratedClassifierCV.ts:340 (opens in a new tab)
feature_names_in_
Names of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/calibration/CalibratedClassifierCV.ts:394 (opens in a new tab)
n_features_in_
Number of features seen during fit. Only defined if the underlying estimator exposes such an attribute when fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/calibration/CalibratedClassifierCV.ts:367 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/calibration/CalibratedClassifierCV.ts:76 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/calibration/CalibratedClassifierCV.ts:80 (opens in a new tab)
Methods
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/calibration/CalibratedClassifierCV.ts:137 (opens in a new tab)
fit()
Fit the calibrated model.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training data. |
opts.fit_params? | any | Parameters to pass to the fit method of the underlying classifier. |
opts.sample_weight? | ArrayLike | Sample weights. If undefined, then samples are equally weighted. |
opts.y? | ArrayLike | Target values. |
Returns
Promise<any>
Defined in: generated/calibration/CalibratedClassifierCV.ts:154 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/calibration/CalibratedClassifierCV.ts:89 (opens in a new tab)
predict()
Predict the target of new samples.
The predicted class is the class that has the highest probability, and can thus be different from the prediction of the uncalibrated classifier.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The samples, as accepted by estimator.predict. |
Returns
Promise<ArrayLike>
Defined in: generated/calibration/CalibratedClassifierCV.ts:212 (opens in a new tab)
predict_proba()
Calibrated probabilities of classification.
This function returns calibrated probabilities of classification according to each class on an array of test vectors X.
Signature
predict_proba(opts: object): Promise<ArrayLike[]>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The samples, as accepted by estimator.predict\_proba. |
Returns
Promise<ArrayLike[]>
Defined in: generated/calibration/CalibratedClassifierCV.ts:251 (opens in a new tab)
score()
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.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Test samples. |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True labels for X. |
Returns
Promise<number>
Defined in: generated/calibration/CalibratedClassifierCV.ts:291 (opens in a new tab)