KernelRidge
Kernel ridge regression.
Kernel ridge regression (KRR) combines ridge regression (linear least squares with l2-norm regularization) with the kernel trick. It thus learns a linear function in the space induced by the respective kernel and the data. For non-linear kernels, this corresponds to a non-linear function in the original space.
The form of the model learned by KRR is identical to support vector regression (SVR). However, different loss functions are used: KRR uses squared error loss while support vector regression uses epsilon-insensitive loss, both combined with l2 regularization. In contrast to SVR, fitting a KRR model can be done in closed-form and is typically faster for medium-sized datasets. On the other hand, the learned model is non-sparse and thus slower than SVR, which learns a sparse model for epsilon > 0, at prediction-time.
This estimator has built-in support for multi-variate regression (i.e., when y is a 2d-array of shape [n_samples, n_targets]).
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new KernelRidge(opts?: object): KernelRidge;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.alpha? | number | ArrayLike | 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 an array is passed, penalties are assumed to be specific to the targets. Hence they must correspond in number. See Ridge regression and classification for formula. Default Value 1 |
opts.coef0? | number | Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels. Default Value 1 |
opts.degree? | number | Degree of the polynomial kernel. Ignored by other kernels. Default Value 3 |
opts.gamma? | number | Gamma parameter for the RBF, laplacian, polynomial, exponential chi2 and sigmoid kernels. Interpretation of the default value is left to the kernel; see the documentation for sklearn.metrics.pairwise. Ignored by other kernels. |
opts.kernel? | string | Kernel mapping used internally. This parameter is directly passed to pairwise\_kernels. If kernel is a string, it must be one of the metrics in pairwise.PAIRWISE\_KERNEL\_FUNCTIONS or “precomputed”. If kernel is “precomputed”, X is assumed to be a kernel matrix. Alternatively, if kernel is a callable function, it is called on each pair of instances (rows) and the resulting value recorded. The callable should take two rows from X as input and return the corresponding kernel value as a single number. This means that callables from sklearn.metrics.pairwise are not allowed, as they operate on matrices, not single samples. Use the string identifying the kernel instead. Default Value 'linear' |
opts.kernel_params? | any | Additional parameters (keyword arguments) for kernel function passed as callable object. |
Returns
Defined in: generated/kernel_ridge/KernelRidge.ts:29 (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/kernel_ridge/KernelRidge.ts:131 (opens in a new tab)
fit()
Fit Kernel Ridge regression model.
Signature
fit(opts: object): Promise<any>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Training data. If kernel == “precomputed” this is instead a precomputed kernel matrix, of shape (n_samples, n_samples). |
opts.sample_weight? | number | ArrayLike | Individual weights for each sample, ignored if undefined is passed. |
opts.y? | ArrayLike | Target values. |
Returns
Promise<any>
Defined in: generated/kernel_ridge/KernelRidge.ts:148 (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/kernel_ridge/KernelRidge.ts:197 (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/kernel_ridge/KernelRidge.ts:85 (opens in a new tab)
predict()
Predict using the kernel ridge model.
Signature
predict(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike | Samples. If kernel == “precomputed” this is instead a precomputed kernel matrix, shape = [n_samples, n_samples_fitted], where n_samples_fitted is the number of samples used in the fitting for this estimator. |
Returns
Promise<ArrayLike>
Defined in: generated/kernel_ridge/KernelRidge.ts:232 (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/kernel_ridge/KernelRidge.ts:267 (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/kernel_ridge/KernelRidge.ts:318 (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/kernel_ridge/KernelRidge.ts:355 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/kernel_ridge/KernelRidge.ts:27 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/kernel_ridge/KernelRidge.ts:26 (opens in a new tab)
_py
PythonBridge
Defined in: generated/kernel_ridge/KernelRidge.ts:25 (opens in a new tab)
id
string
Defined in: generated/kernel_ridge/KernelRidge.ts:22 (opens in a new tab)
opts
any
Defined in: generated/kernel_ridge/KernelRidge.ts:23 (opens in a new tab)
Accessors
X_fit_
Training data, which is also required for prediction. If kernel == “precomputed” this is instead the precomputed training matrix, of shape (n_samples, n_samples).
Signature
X_fit_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/kernel_ridge/KernelRidge.ts:413 (opens in a new tab)
dual_coef_
Representation of weight vector(s) in kernel space
Signature
dual_coef_(): Promise<ArrayLike>;Returns
Promise<ArrayLike>
Defined in: generated/kernel_ridge/KernelRidge.ts:388 (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/kernel_ridge/KernelRidge.ts:461 (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/kernel_ridge/KernelRidge.ts:436 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/kernel_ridge/KernelRidge.ts:72 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/kernel_ridge/KernelRidge.ts:76 (opens in a new tab)