package sklearn

You can search for identifiers within the package.

in-package search v0.2.0

sklearn
- Sklearn

Legend:
Library
Module
Module type
Parameter
Class
Class type

type t

val of_pyobject : Py.Object.t -> t

val to_pyobject : t -> Py.Object.t

val create : 
  ?alpha:[ `F of float | `Arr of Arr.t ] ->
  ?kernel:[ `S of string | `Callable of Py.Object.t ] ->
  ?gamma:float ->
  ?degree:float ->
  ?coef0:float ->
  ?kernel_params:Dict.t ->
  unit ->
  t

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 :ref:`User Guide <kernel_ridge>`.

Parameters ---------- alpha : float, array-like, shape = n_targets Small positive values of alpha improve the conditioning of the problem and reduce the variance of the estimates. Alpha corresponds to ``(2*C)^-1`` 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.

kernel : string or callable, default="linear" Kernel mapping used internally. A callable should accept two arguments and the keyword arguments passed to this object as kernel_params, and should return a floating point number. Set to "precomputed" in order to pass a precomputed kernel matrix to the estimator methods instead of samples.

gamma : float, default=None 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.

degree : float, default=3 Degree of the polynomial kernel. Ignored by other kernels.

coef0 : float, default=1 Zero coefficient for polynomial and sigmoid kernels. Ignored by other kernels.

kernel_params : mapping of string to any, optional Additional parameters (keyword arguments) for kernel function passed as callable object.

Attributes ---------- dual_coef_ : array, shape = n_samples or n_samples, n_targets Representation of weight vector(s) in kernel space

X_fit_ : array-like, sparse matrix of shape (n_samples, n_features) Training data, which is also required for prediction. If kernel == "precomputed" this is instead the precomputed training matrix, shape = n_samples, n_samples.

References ---------- * Kevin P. Murphy "Machine Learning: A Probabilistic Perspective", The MIT Press chapter 14.4.3, pp. 492-493

See also -------- sklearn.linear_model.Ridge: Linear ridge regression. sklearn.svm.SVR: Support Vector Regression implemented using libsvm.

Examples -------- >>> from sklearn.kernel_ridge import KernelRidge >>> import numpy as np >>> n_samples, n_features = 10, 5 >>> rng = np.random.RandomState(0) >>> y = rng.randn(n_samples) >>> X = rng.randn(n_samples, n_features) >>> clf = KernelRidge(alpha=1.0) >>> clf.fit(X, y) KernelRidge(alpha=1.0)

val fit : 
  ?y:Arr.t ->
  ?sample_weight:[ `F of float | `Arr of Arr.t ] ->
  x:Arr.t ->
  t ->
  t

Fit Kernel Ridge regression model

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) Training data. If kernel == "precomputed" this is instead a precomputed kernel matrix, shape = n_samples, n_samples.

y : array-like of shape (n_samples,) or (n_samples, n_targets) Target values

sample_weight : float or array-like of shape n_samples Individual weights for each sample, ignored if None is passed.

Returns ------- self : returns an instance of self.

val get_params : ?deep:bool -> t -> Dict.t

Get parameters for this estimator.

Parameters ---------- deep : bool, default=True If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns ------- params : mapping of string to any Parameter names mapped to their values.

val predict : x:Arr.t -> t -> Arr.t

Predict using the kernel ridge model

Parameters ---------- X : array-like, sparse matrix of shape (n_samples, n_features) 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 ------- C : ndarray of shape (n_samples,) or (n_samples, n_targets) Returns predicted values.

val score : ?sample_weight:Arr.t -> x:Arr.t -> y:Arr.t -> t -> float

Return the coefficient of determination R^2 of the prediction.

The coefficient R^2 is defined as (1 - 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 ---------- X : array-like of shape (n_samples, n_features) Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead, shape = (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.

y : array-like of shape (n_samples,) or (n_samples, n_outputs) True values for X.

sample_weight : array-like of shape (n_samples,), default=None Sample weights.

Returns ------- score : float R^2 of self.predict(X) wrt. y.

Notes ----- The R2 score used when calling ``score`` on a regressor will use ``multioutput='uniform_average'`` from version 0.23 to keep consistent with :func:`~sklearn.metrics.r2_score`. This will influence the ``score`` method of all the multioutput regressors (except for :class:`~sklearn.multioutput.MultiOutputRegressor`). To specify the default value manually and avoid the warning, please either call :func:`~sklearn.metrics.r2_score` directly or make a custom scorer with :func:`~sklearn.metrics.make_scorer` (the built-in scorer ``'r2'`` uses ``multioutput='uniform_average'``).

val set_params : ?params:(string * Py.Object.t) list -> t -> t

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``<component>__<parameter>`` so that it's possible to update each component of a nested object.

Parameters ---------- **params : dict Estimator parameters.

Returns ------- self : object Estimator instance.

val dual_coef_ : t -> Arr.t

Attribute dual_coef_: get value or raise Not_found if None.

val dual_coef_opt : t -> Arr.t option

Attribute dual_coef_: get value as an option.

val x_fit_ : t -> Arr.t

Attribute X_fit_: get value or raise Not_found if None.

val x_fit_opt : t -> Arr.t option

Attribute X_fit_: get value as an option.

val to_string : t -> string

Print the object to a human-readable representation.

val show : t -> string

Print the object to a human-readable representation.

val pp : Format.formatter -> t -> unit

Pretty-print the object to a formatter.