Ridge
Linear least squares with l2 regularization.
Minimizes the objective function:
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new Ridge(opts?: object): Ridge;Parameters
| Name | 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. Default Value 1 |
opts.copy_X? | boolean | If true, X will be copied; else, it may be overwritten. Default Value true |
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). Default Value true |
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. Default Value false |
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: Default Value 'auto' |
opts.tol? | number | The precision of the solution (coef\_) is determined by tol which specifies a different convergence criterion for each solver: Default Value 0.0001 |
Returns
Defined in: generated/linear_model/Ridge.ts:23 (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/Ridge.ts:152 (opens in a new tab)
fit()
Fit Ridge regression model.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training data. |
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. |
Returns
Promise<any>
Defined in: generated/linear_model/Ridge.ts:169 (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/Ridge.ts:218 (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/Ridge.ts:105 (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/Ridge.ts:251 (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/Ridge.ts:284 (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/Ridge.ts:335 (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/Ridge.ts:372 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/linear_model/Ridge.ts:21 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/linear_model/Ridge.ts:20 (opens in a new tab)
_py
PythonBridge
Defined in: generated/linear_model/Ridge.ts:19 (opens in a new tab)
id
string
Defined in: generated/linear_model/Ridge.ts:16 (opens in a new tab)
opts
any
Defined in: generated/linear_model/Ridge.ts:17 (opens in a new tab)
Accessors
coef_
Weight vector(s).
Signature
coef_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/linear_model/Ridge.ts:405 (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/Ridge.ts:495 (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/Ridge.ts:427 (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/Ridge.ts:472 (opens in a new tab)
n_iter_
Actual number of iterations for each target. Available only for sag and lsqr solvers. Other solvers will return undefined.
Signature
n_iter_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/linear_model/Ridge.ts:450 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/linear_model/Ridge.ts:92 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/linear_model/Ridge.ts:96 (opens in a new tab)