RidgeClassifierCV
Ridge classifier with built-in cross-validation.
See glossary entry for cross-validation estimator.
By default, it performs Leave-One-Out Cross-Validation. Currently, only the n_features > n_samples case is handled efficiently.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new RidgeClassifierCV(opts?: object): RidgeClassifierCV;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.alphas? | ArrayLike | Array of alpha values to try. Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization. Alpha corresponds to 1 / (2C) in other linear models such as LogisticRegression or LinearSVC. |
opts.class_weight? | any | Weights associated with classes in the form {class\_label: weight}. If not given, all classes are supposed to have weight one. The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n\_samples / (n\_classes \* np.bincount(y)). |
opts.cv? | number | Determines the cross-validation splitting strategy. Possible inputs for cv are: |
opts.fit_intercept? | boolean | Whether to calculate the intercept for this model. If set to false, no intercept will be used in calculations (i.e. data is expected to be centered). Default Value true |
opts.scoring? | string | A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). |
opts.store_cv_values? | boolean | Flag indicating if the cross-validation values corresponding to each alpha should be stored in the cv\_values\_ attribute (see below). This flag is only compatible with cv=None (i.e. using Leave-One-Out Cross-Validation). Default Value false |
Returns
Defined in: generated/linear_model/RidgeClassifierCV.ts:27 (opens in a new tab)
Methods
decision_function()
Predict confidence scores for samples.
The confidence score for a sample is proportional to the signed distance of that sample to the hyperplane.
Signature
decision_function(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | The data matrix for which we want to get the confidence scores. |
Returns
Promise<ArrayLike>
Defined in: generated/linear_model/RidgeClassifierCV.ts:148 (opens in a new tab)
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/linear_model/RidgeClassifierCV.ts:129 (opens in a new tab)
fit()
Fit Ridge classifier with cv.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | 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. When using GCV, will be cast to float64 if necessary. |
opts.sample_weight? | number | ArrayLike | Individual weights for each sample. If given a float, every sample will have the same weight. |
opts.y? | ArrayLike | Target values. Will be cast to X’s dtype if necessary. |
Returns
Promise<any>
Defined in: generated/linear_model/RidgeClassifierCV.ts:186 (opens in a new tab)
get_metadata_routing()
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Signature
get_metadata_routing(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns
Promise<any>
Defined in: generated/linear_model/RidgeClassifierCV.ts:237 (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/linear_model/RidgeClassifierCV.ts:81 (opens in a new tab)
predict()
Predict class labels for samples in X.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | The data matrix for which we want to predict the targets. |
Returns
Promise<ArrayLike>
Defined in: generated/linear_model/RidgeClassifierCV.ts:275 (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/linear_model/RidgeClassifierCV.ts:312 (opens in a new tab)
set_fit_request()
Request metadata passed to the fit 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:
Signature
set_fit_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in fit. |
Returns
Promise<any>
Defined in: generated/linear_model/RidgeClassifierCV.ts:365 (opens in a new tab)
set_score_request()
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:
Signature
set_score_request(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | string | boolean | Metadata routing for sample\_weight parameter in score. |
Returns
Promise<any>
Defined in: generated/linear_model/RidgeClassifierCV.ts:407 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/linear_model/RidgeClassifierCV.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/linear_model/RidgeClassifierCV.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/linear_model/RidgeClassifierCV.ts:23 (opens in a new tab)
id
string
Defined in: generated/linear_model/RidgeClassifierCV.ts:20 (opens in a new tab)
opts
any
Defined in: generated/linear_model/RidgeClassifierCV.ts:21 (opens in a new tab)
Accessors
alpha_
Estimated regularization parameter.
Signature
alpha_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/RidgeClassifierCV.ts:528 (opens in a new tab)
best_score_
Score of base estimator with best alpha.
Signature
best_score_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/RidgeClassifierCV.ts:555 (opens in a new tab)
coef_
Coefficient of the features in the decision function.
coef\_ is of shape (1, n_features) when the given problem is binary.
Signature
coef_(): Promise<ArrayLike[]>;Returns
Promise<ArrayLike[]>
Defined in: generated/linear_model/RidgeClassifierCV.ts:474 (opens in a new tab)
cv_values_
Cross-validation values for each alpha (only if store\_cv\_values=True and cv=None). After fit() has been called, this attribute will contain the mean squared errors if scoring is None otherwise it will contain standardized per point prediction values.
Signature
cv_values_(): Promise<ArrayLike[][]>;Returns
Promise<ArrayLike[][]>
Defined in: generated/linear_model/RidgeClassifierCV.ts:445 (opens in a new tab)
feature_names_in_
Names of features seen during fit. Defined only when X has feature names that are all strings.
Signature
feature_names_in_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/linear_model/RidgeClassifierCV.ts:609 (opens in a new tab)
intercept_
Independent term in decision function. Set to 0.0 if fit\_intercept \= False.
Signature
intercept_(): Promise<number | ArrayLike>;Returns
Promise<number | ArrayLike>
Defined in: generated/linear_model/RidgeClassifierCV.ts:501 (opens in a new tab)
n_features_in_
Number of features seen during fit.
Signature
n_features_in_(): Promise<number>;Returns
Promise<number>
Defined in: generated/linear_model/RidgeClassifierCV.ts:582 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/linear_model/RidgeClassifierCV.ts:68 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/linear_model/RidgeClassifierCV.ts:72 (opens in a new tab)