RidgeCV
Ridge regression with built-in cross-validation.
See glossary entry for cross-validation estimator.
By default, it performs efficient Leave-One-Out Cross-Validation.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new RidgeCV(opts?: object): RidgeCV;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha_per_target? | boolean | Flag indicating whether to optimize the alpha value (picked from the alphas parameter list) for each target separately (for multi-output settings: multiple prediction targets). When set to true, after fitting, the alpha\_ attribute will contain a value for each target. When set to false, a single alpha is used for all targets. Default Value false |
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. If using Leave-One-Out cross-validation, alphas must be positive. |
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.gcv_mode? | "auto" | "svd" | "eigen" | Flag indicating which strategy to use when performing Leave-One-Out Cross-Validation. Options are: Default Value 'auto' |
opts.scoring? | string | A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If undefined, the negative mean squared error if cv is ‘auto’ or undefined (i.e. when using leave-one-out cross-validation), and r2 score otherwise. |
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/RidgeCV.ts:27 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/linear_model/RidgeCV.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/linear_model/RidgeCV.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/linear_model/RidgeCV.ts:23 (opens in a new tab)
id
string
Defined in: generated/linear_model/RidgeCV.ts:20 (opens in a new tab)
opts
any
Defined in: generated/linear_model/RidgeCV.ts:21 (opens in a new tab)
Accessors
alpha_
Estimated regularization parameter, or, if alpha\_per\_target=True, the estimated regularization parameter for each target.
Signature
alpha_(): Promise<number | ArrayLike>;Returns
Promise<number | ArrayLike>
Defined in: generated/linear_model/RidgeCV.ts:345 (opens in a new tab)
best_score_
Score of base estimator with best alpha, or, if alpha\_per\_target=True, a score for each target.
Signature
best_score_(): Promise<number | ArrayLike>;Returns
Promise<number | ArrayLike>
Defined in: generated/linear_model/RidgeCV.ts:367 (opens in a new tab)
coef_
Weight vector(s).
Signature
coef_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/linear_model/RidgeCV.ts:300 (opens in a new tab)
cv_values_
Cross-validation values for each alpha (only available 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/RidgeCV.ts:277 (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/RidgeCV.ts:415 (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/RidgeCV.ts:322 (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/RidgeCV.ts:390 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/linear_model/RidgeCV.ts:75 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/linear_model/RidgeCV.ts:79 (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/linear_model/RidgeCV.ts:133 (opens in a new tab)
fit()
Fit Ridge regression model with cv.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training data. If 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/RidgeCV.ts:150 (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/RidgeCV.ts:88 (opens in a new tab)
predict()
Predict using the linear model.
Signature
predict(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Samples. |
Returns
Promise<any>
Defined in: generated/linear_model/RidgeCV.ts:197 (opens in a new tab)
score()
Return the coefficient of determination of the prediction.
The coefficient of determination \(R^2\) is defined as \((1 - \frac{u}{v})\), where \(u\) is the residual sum of squares ((y\_true \- y\_pred)\*\* 2).sum() and \(v\) is the total sum of squares ((y\_true \- y\_true.mean()) \*\* 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a \(R^2\) score of 0.0.
Signature
score(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n\_samples, n\_samples\_fitted), where n\_samples\_fitted is the number of samples used in the fitting for the estimator. |
opts.sample_weight? | ArrayLike | Sample weights. |
opts.y? | ArrayLike | True values for X. |
Returns
Promise<number>
Defined in: generated/linear_model/RidgeCV.ts:230 (opens in a new tab)