Class: Ridge
Linear least squares with l2 regularization.
Minimizes the objective function:
Constructors
new Ridge()
new Ridge(
opts?):Ridge
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | Constant that multiplies the L2 term, controlling regularization strength. alpha must be a non-negative float i.e. in \[0, inf). When alpha \= 0, the objective is equivalent to ordinary least squares, solved by the LinearRegression object. For numerical reasons, using alpha \= 0 with the Ridge object is not advised. Instead, you should use the LinearRegression object. If an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number. |
opts.copy_X? | boolean | If true, X will be copied; else, it may be overwritten. |
opts.fit_intercept? | boolean | Whether to fit the intercept for this model. If set to false, no intercept will be used in calculations (i.e. X and y are expected to be centered). |
opts.max_iter? | number | Maximum number of iterations for conjugate gradient solver. For ‘sparse_cg’ and ‘lsqr’ solvers, the default value is determined by scipy.sparse.linalg. For ‘sag’ solver, the default value is 1000. For ‘lbfgs’ solver, the default value is 15000. |
opts.positive? | boolean | When set to true, forces the coefficients to be positive. Only ‘lbfgs’ solver is supported in this case. |
opts.random_state? | number | Used when solver == ‘sag’ or ‘saga’ to shuffle the data. See Glossary for details. |
opts.solver? | "auto" | "svd" | "lsqr" | "lbfgs" | "sag" | "saga" | "cholesky" | "sparse_cg" | Solver to use in the computational routines: |
opts.tol? | number | The precision of the solution (coef_) is determined by tol which specifies a different convergence criterion for each solver: |
Returns Ridge
Defined in generated/linear_model/Ridge.ts:23
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/linear_model/Ridge.ts:21 |
_isInitialized | boolean | false | generated/linear_model/Ridge.ts:20 |
_py | PythonBridge | undefined | generated/linear_model/Ridge.ts:19 |
id | string | undefined | generated/linear_model/Ridge.ts:16 |
opts | any | undefined | generated/linear_model/Ridge.ts:17 |
Accessors
coef_
Get Signature
get coef_():
Promise<ArrayLike>
Weight vector(s).
Returns Promise<ArrayLike>
Defined in generated/linear_model/Ridge.ts:383
feature_names_in_
Get Signature
get feature_names_in_():
Promise<ArrayLike>
Names of features seen during fit. Defined only when X has feature names that are all strings.
Returns Promise<ArrayLike>
Defined in generated/linear_model/Ridge.ts:473
intercept_
Get Signature
get intercept_():
Promise<number|ArrayLike>
Independent term in decision function. Set to 0.0 if fit_intercept \= False.
Returns Promise<number | ArrayLike>
Defined in generated/linear_model/Ridge.ts:405
n_features_in_
Get Signature
get n_features_in_():
Promise<number>
Number of features seen during fit.
Returns Promise<number>
Defined in generated/linear_model/Ridge.ts:450
n_iter_
Get Signature
get n_iter_():
Promise<ArrayLike>
Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return undefined.
Returns Promise<ArrayLike>
Defined in generated/linear_model/Ridge.ts:428
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/linear_model/Ridge.ts:92
solver_
Get Signature
get solver_():
Promise<string>
The solver that was used at fit time by the computational routines.
Returns Promise<string>
Defined in generated/linear_model/Ridge.ts:498
Methods
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/linear_model/Ridge.ts:143
fit()
fit(
opts):Promise<any>
Fit Ridge regression model.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | number | ArrayLike | Individual weights for each sample. If given a float, every sample will have the same weight. |
opts.X? | ArrayLike | Training data. |
opts.y? | ArrayLike | Target values. |
Returns Promise<any>
Defined in generated/linear_model/Ridge.ts:160
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 MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/linear_model/Ridge.ts:204
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/linear_model/Ridge.ts:105
predict()
predict(
opts):Promise<any>
Predict using the linear model.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Samples. |
Returns Promise<any>
Defined in generated/linear_model/Ridge.ts:236
score()
score(
opts):Promise<number>
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.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.sample_weight? | ArrayLike | Sample weights. |
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.y? | ArrayLike | True values for X. |
Returns Promise<number>
Defined in generated/linear_model/Ridge.ts:269
set_fit_request()
set_fit_request(
opts):Promise<any>
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:
Parameters
| Parameter | 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/Ridge.ts:315
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/linear_model/Ridge.ts:351