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Classes
KernelRidge

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

NameTypeDescription
opts?object-
opts.alpha?number | ArrayLikeRegularization 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?numberZero coefficient for polynomial and sigmoid kernels. Ignored by other kernels. Default Value 1
opts.degree?numberDegree of the polynomial kernel. Ignored by other kernels. Default Value 3
opts.gamma?numberGamma 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?stringKernel mapping used internally. This parameter is directly passed to pairwise\_kernel. 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?anyAdditional parameters (keyword arguments) for kernel function passed as callable object.

Returns

KernelRidge

Defined in: generated/kernel_ridge/KernelRidge.ts:29 (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:302 (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: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/kernel_ridge/KernelRidge.ts:350 (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:325 (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

NameType
pythonBridgePythonBridge

Returns

void

Defined in: generated/kernel_ridge/KernelRidge.ts:76 (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

NameTypeDescription
optsobject-
opts.X?ArrayLikeTraining data. If kernel == “precomputed” this is instead a precomputed kernel matrix, of shape (n_samples, n_samples).
opts.sample_weight?number | ArrayLikeIndividual weights for each sample, ignored if undefined is passed.
opts.y?ArrayLikeTarget values.

Returns

Promise<any>

Defined in: generated/kernel_ridge/KernelRidge.ts:148 (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

NameType
pyPythonBridge

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

NameTypeDescription
optsobject-
opts.X?ArrayLikeSamples. 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:195 (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

NameTypeDescription
optsobject-
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?ArrayLikeSample weights.
opts.y?ArrayLikeTrue values for X.

Returns

Promise<number>

Defined in: generated/kernel_ridge/KernelRidge.ts:230 (opens in a new tab)