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is_stationary() [source]
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.product#sklearn.gaussian_process.kernels.Product.is_stationary |
property n_dims
Returns the number of non-fixed hyperparameters of the kernel. | sklearn.modules.generated.sklearn.gaussian_process.kernels.product#sklearn.gaussian_process.kernels.Product.n_dims |
property requires_vector_input
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.product#sklearn.gaussian_process.kernels.Product.requires_vector_input |
set_params(**params) [source]
Set the parameters of this kernel. The method works on simple kernels as well as on nested kernels. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Returns
self | sklearn.modules.generated.sklearn.gaussian_process.kernels.product#sklearn.gaussian_process.kernels.Product.set_params |
property theta
Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel’s hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a l... | sklearn.modules.generated.sklearn.gaussian_process.kernels.product#sklearn.gaussian_process.kernels.Product.theta |
__call__(X, Y=None, eval_gradient=False) [source]
Return the kernel k(X, Y) and optionally its gradient. Parameters
Xarray-like of shape (n_samples_X, n_features) or list of object
Left argument of the returned kernel k(X, Y)
Yarray-like of shape (n_samples_Y, n_features) or list of object, default=None
Rig... | sklearn.modules.generated.sklearn.gaussian_process.kernels.product#sklearn.gaussian_process.kernels.Product.__call__ |
class sklearn.gaussian_process.kernels.RationalQuadratic(length_scale=1.0, alpha=1.0, length_scale_bounds=1e-05, 100000.0, alpha_bounds=1e-05, 100000.0) [source]
Rational Quadratic kernel. The RationalQuadratic kernel can be seen as a scale mixture (an infinite sum) of RBF kernels with different characteristic length... | sklearn.modules.generated.sklearn.gaussian_process.kernels.rationalquadratic#sklearn.gaussian_process.kernels.RationalQuadratic |
sklearn.gaussian_process.kernels.RationalQuadratic
class sklearn.gaussian_process.kernels.RationalQuadratic(length_scale=1.0, alpha=1.0, length_scale_bounds=1e-05, 100000.0, alpha_bounds=1e-05, 100000.0) [source]
Rational Quadratic kernel. The RationalQuadratic kernel can be seen as a scale mixture (an infinite sum... | sklearn.modules.generated.sklearn.gaussian_process.kernels.rationalquadratic |
property bounds
Returns the log-transformed bounds on the theta. Returns
boundsndarray of shape (n_dims, 2)
The log-transformed bounds on the kernel’s hyperparameters theta | sklearn.modules.generated.sklearn.gaussian_process.kernels.rationalquadratic#sklearn.gaussian_process.kernels.RationalQuadratic.bounds |
clone_with_theta(theta) [source]
Returns a clone of self with given hyperparameters theta. Parameters
thetandarray of shape (n_dims,)
The hyperparameters | sklearn.modules.generated.sklearn.gaussian_process.kernels.rationalquadratic#sklearn.gaussian_process.kernels.RationalQuadratic.clone_with_theta |
diag(X) [source]
Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters
Xndarray of shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y) Retu... | sklearn.modules.generated.sklearn.gaussian_process.kernels.rationalquadratic#sklearn.gaussian_process.kernels.RationalQuadratic.diag |
get_params(deep=True) [source]
Get parameters of this kernel. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.gaussian_process.kernels.rationalquadratic#sklearn.gaussian_process.kernels.RationalQuadratic.get_params |
property hyperparameters
Returns a list of all hyperparameter specifications. | sklearn.modules.generated.sklearn.gaussian_process.kernels.rationalquadratic#sklearn.gaussian_process.kernels.RationalQuadratic.hyperparameters |
is_stationary() [source]
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.rationalquadratic#sklearn.gaussian_process.kernels.RationalQuadratic.is_stationary |
property n_dims
Returns the number of non-fixed hyperparameters of the kernel. | sklearn.modules.generated.sklearn.gaussian_process.kernels.rationalquadratic#sklearn.gaussian_process.kernels.RationalQuadratic.n_dims |
property requires_vector_input
Returns whether the kernel is defined on fixed-length feature vectors or generic objects. Defaults to True for backward compatibility. | sklearn.modules.generated.sklearn.gaussian_process.kernels.rationalquadratic#sklearn.gaussian_process.kernels.RationalQuadratic.requires_vector_input |
set_params(**params) [source]
Set the parameters of this kernel. The method works on simple kernels as well as on nested kernels. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Returns
self | sklearn.modules.generated.sklearn.gaussian_process.kernels.rationalquadratic#sklearn.gaussian_process.kernels.RationalQuadratic.set_params |
property theta
Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel’s hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a l... | sklearn.modules.generated.sklearn.gaussian_process.kernels.rationalquadratic#sklearn.gaussian_process.kernels.RationalQuadratic.theta |
__call__(X, Y=None, eval_gradient=False) [source]
Return the kernel k(X, Y) and optionally its gradient. Parameters
Xndarray of shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y)
Yndarray of shape (n_samples_Y, n_features), default=None
Right argument of the returned kernel k(X, Y)... | sklearn.modules.generated.sklearn.gaussian_process.kernels.rationalquadratic#sklearn.gaussian_process.kernels.RationalQuadratic.__call__ |
class sklearn.gaussian_process.kernels.RBF(length_scale=1.0, length_scale_bounds=1e-05, 100000.0) [source]
Radial-basis function kernel (aka squared-exponential kernel). The RBF kernel is a stationary kernel. It is also known as the “squared exponential” kernel. It is parameterized by a length scale parameter \(l>0\)... | sklearn.modules.generated.sklearn.gaussian_process.kernels.rbf#sklearn.gaussian_process.kernels.RBF |
sklearn.gaussian_process.kernels.RBF
class sklearn.gaussian_process.kernels.RBF(length_scale=1.0, length_scale_bounds=1e-05, 100000.0) [source]
Radial-basis function kernel (aka squared-exponential kernel). The RBF kernel is a stationary kernel. It is also known as the “squared exponential” kernel. It is parameteri... | sklearn.modules.generated.sklearn.gaussian_process.kernels.rbf |
property bounds
Returns the log-transformed bounds on the theta. Returns
boundsndarray of shape (n_dims, 2)
The log-transformed bounds on the kernel’s hyperparameters theta | sklearn.modules.generated.sklearn.gaussian_process.kernels.rbf#sklearn.gaussian_process.kernels.RBF.bounds |
clone_with_theta(theta) [source]
Returns a clone of self with given hyperparameters theta. Parameters
thetandarray of shape (n_dims,)
The hyperparameters | sklearn.modules.generated.sklearn.gaussian_process.kernels.rbf#sklearn.gaussian_process.kernels.RBF.clone_with_theta |
diag(X) [source]
Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters
Xndarray of shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y) Retu... | sklearn.modules.generated.sklearn.gaussian_process.kernels.rbf#sklearn.gaussian_process.kernels.RBF.diag |
get_params(deep=True) [source]
Get parameters of this kernel. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.gaussian_process.kernels.rbf#sklearn.gaussian_process.kernels.RBF.get_params |
property hyperparameters
Returns a list of all hyperparameter specifications. | sklearn.modules.generated.sklearn.gaussian_process.kernels.rbf#sklearn.gaussian_process.kernels.RBF.hyperparameters |
is_stationary() [source]
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.rbf#sklearn.gaussian_process.kernels.RBF.is_stationary |
property n_dims
Returns the number of non-fixed hyperparameters of the kernel. | sklearn.modules.generated.sklearn.gaussian_process.kernels.rbf#sklearn.gaussian_process.kernels.RBF.n_dims |
property requires_vector_input
Returns whether the kernel is defined on fixed-length feature vectors or generic objects. Defaults to True for backward compatibility. | sklearn.modules.generated.sklearn.gaussian_process.kernels.rbf#sklearn.gaussian_process.kernels.RBF.requires_vector_input |
set_params(**params) [source]
Set the parameters of this kernel. The method works on simple kernels as well as on nested kernels. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Returns
self | sklearn.modules.generated.sklearn.gaussian_process.kernels.rbf#sklearn.gaussian_process.kernels.RBF.set_params |
property theta
Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel’s hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a l... | sklearn.modules.generated.sklearn.gaussian_process.kernels.rbf#sklearn.gaussian_process.kernels.RBF.theta |
__call__(X, Y=None, eval_gradient=False) [source]
Return the kernel k(X, Y) and optionally its gradient. Parameters
Xndarray of shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y)
Yndarray of shape (n_samples_Y, n_features), default=None
Right argument of the returned kernel k(X, Y)... | sklearn.modules.generated.sklearn.gaussian_process.kernels.rbf#sklearn.gaussian_process.kernels.RBF.__call__ |
class sklearn.gaussian_process.kernels.Sum(k1, k2) [source]
The Sum kernel takes two kernels \(k_1\) and \(k_2\) and combines them via \[k_{sum}(X, Y) = k_1(X, Y) + k_2(X, Y)\] Note that the __add__ magic method is overridden, so Sum(RBF(), RBF()) is equivalent to using the + operator with RBF() + RBF(). Read more i... | sklearn.modules.generated.sklearn.gaussian_process.kernels.sum#sklearn.gaussian_process.kernels.Sum |
sklearn.gaussian_process.kernels.Sum
class sklearn.gaussian_process.kernels.Sum(k1, k2) [source]
The Sum kernel takes two kernels \(k_1\) and \(k_2\) and combines them via \[k_{sum}(X, Y) = k_1(X, Y) + k_2(X, Y)\] Note that the __add__ magic method is overridden, so Sum(RBF(), RBF()) is equivalent to using the + o... | sklearn.modules.generated.sklearn.gaussian_process.kernels.sum |
property bounds
Returns the log-transformed bounds on the theta. Returns
boundsndarray of shape (n_dims, 2)
The log-transformed bounds on the kernel’s hyperparameters theta | sklearn.modules.generated.sklearn.gaussian_process.kernels.sum#sklearn.gaussian_process.kernels.Sum.bounds |
clone_with_theta(theta) [source]
Returns a clone of self with given hyperparameters theta. Parameters
thetandarray of shape (n_dims,)
The hyperparameters | sklearn.modules.generated.sklearn.gaussian_process.kernels.sum#sklearn.gaussian_process.kernels.Sum.clone_with_theta |
diag(X) [source]
Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters
Xarray-like of shape (n_samples_X, n_features) or list of object
Argument to the kernel. Retu... | sklearn.modules.generated.sklearn.gaussian_process.kernels.sum#sklearn.gaussian_process.kernels.Sum.diag |
get_params(deep=True) [source]
Get parameters of this kernel. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.gaussian_process.kernels.sum#sklearn.gaussian_process.kernels.Sum.get_params |
property hyperparameters
Returns a list of all hyperparameter. | sklearn.modules.generated.sklearn.gaussian_process.kernels.sum#sklearn.gaussian_process.kernels.Sum.hyperparameters |
is_stationary() [source]
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.sum#sklearn.gaussian_process.kernels.Sum.is_stationary |
property n_dims
Returns the number of non-fixed hyperparameters of the kernel. | sklearn.modules.generated.sklearn.gaussian_process.kernels.sum#sklearn.gaussian_process.kernels.Sum.n_dims |
property requires_vector_input
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.sum#sklearn.gaussian_process.kernels.Sum.requires_vector_input |
set_params(**params) [source]
Set the parameters of this kernel. The method works on simple kernels as well as on nested kernels. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Returns
self | sklearn.modules.generated.sklearn.gaussian_process.kernels.sum#sklearn.gaussian_process.kernels.Sum.set_params |
property theta
Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel’s hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a l... | sklearn.modules.generated.sklearn.gaussian_process.kernels.sum#sklearn.gaussian_process.kernels.Sum.theta |
__call__(X, Y=None, eval_gradient=False) [source]
Return the kernel k(X, Y) and optionally its gradient. Parameters
Xarray-like of shape (n_samples_X, n_features) or list of object
Left argument of the returned kernel k(X, Y)
Yarray-like of shape (n_samples_X, n_features) or list of object, default=None
Rig... | sklearn.modules.generated.sklearn.gaussian_process.kernels.sum#sklearn.gaussian_process.kernels.Sum.__call__ |
class sklearn.gaussian_process.kernels.WhiteKernel(noise_level=1.0, noise_level_bounds=1e-05, 100000.0) [source]
White kernel. The main use-case of this kernel is as part of a sum-kernel where it explains the noise of the signal as independently and identically normally-distributed. The parameter noise_level equals t... | sklearn.modules.generated.sklearn.gaussian_process.kernels.whitekernel#sklearn.gaussian_process.kernels.WhiteKernel |
sklearn.gaussian_process.kernels.WhiteKernel
class sklearn.gaussian_process.kernels.WhiteKernel(noise_level=1.0, noise_level_bounds=1e-05, 100000.0) [source]
White kernel. The main use-case of this kernel is as part of a sum-kernel where it explains the noise of the signal as independently and identically normally-... | sklearn.modules.generated.sklearn.gaussian_process.kernels.whitekernel |
property bounds
Returns the log-transformed bounds on the theta. Returns
boundsndarray of shape (n_dims, 2)
The log-transformed bounds on the kernel’s hyperparameters theta | sklearn.modules.generated.sklearn.gaussian_process.kernels.whitekernel#sklearn.gaussian_process.kernels.WhiteKernel.bounds |
clone_with_theta(theta) [source]
Returns a clone of self with given hyperparameters theta. Parameters
thetandarray of shape (n_dims,)
The hyperparameters | sklearn.modules.generated.sklearn.gaussian_process.kernels.whitekernel#sklearn.gaussian_process.kernels.WhiteKernel.clone_with_theta |
diag(X) [source]
Returns the diagonal of the kernel k(X, X). The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated. Parameters
Xarray-like of shape (n_samples_X, n_features) or list of object
Argument to the kernel. Retu... | sklearn.modules.generated.sklearn.gaussian_process.kernels.whitekernel#sklearn.gaussian_process.kernels.WhiteKernel.diag |
get_params(deep=True) [source]
Get parameters of this kernel. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.gaussian_process.kernels.whitekernel#sklearn.gaussian_process.kernels.WhiteKernel.get_params |
property hyperparameters
Returns a list of all hyperparameter specifications. | sklearn.modules.generated.sklearn.gaussian_process.kernels.whitekernel#sklearn.gaussian_process.kernels.WhiteKernel.hyperparameters |
is_stationary() [source]
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.whitekernel#sklearn.gaussian_process.kernels.WhiteKernel.is_stationary |
property n_dims
Returns the number of non-fixed hyperparameters of the kernel. | sklearn.modules.generated.sklearn.gaussian_process.kernels.whitekernel#sklearn.gaussian_process.kernels.WhiteKernel.n_dims |
property requires_vector_input
Whether the kernel works only on fixed-length feature vectors. | sklearn.modules.generated.sklearn.gaussian_process.kernels.whitekernel#sklearn.gaussian_process.kernels.WhiteKernel.requires_vector_input |
set_params(**params) [source]
Set the parameters of this kernel. The method works on simple kernels as well as on nested kernels. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Returns
self | sklearn.modules.generated.sklearn.gaussian_process.kernels.whitekernel#sklearn.gaussian_process.kernels.WhiteKernel.set_params |
property theta
Returns the (flattened, log-transformed) non-fixed hyperparameters. Note that theta are typically the log-transformed values of the kernel’s hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a l... | sklearn.modules.generated.sklearn.gaussian_process.kernels.whitekernel#sklearn.gaussian_process.kernels.WhiteKernel.theta |
__call__(X, Y=None, eval_gradient=False) [source]
Return the kernel k(X, Y) and optionally its gradient. Parameters
Xarray-like of shape (n_samples_X, n_features) or list of object
Left argument of the returned kernel k(X, Y)
Yarray-like of shape (n_samples_X, n_features) or list of object, default=None
Rig... | sklearn.modules.generated.sklearn.gaussian_process.kernels.whitekernel#sklearn.gaussian_process.kernels.WhiteKernel.__call__ |
sklearn.get_config() [source]
Retrieve current values for configuration set by set_config Returns
configdict
Keys are parameter names that can be passed to set_config. See also
config_context
Context manager for global scikit-learn configuration.
set_config
Set global scikit-learn configuration. | sklearn.modules.generated.sklearn.get_config#sklearn.get_config |
class sklearn.impute.IterativeImputer(estimator=None, *, missing_values=nan, sample_posterior=False, max_iter=10, tol=0.001, n_nearest_features=None, initial_strategy='mean', imputation_order='ascending', skip_complete=False, min_value=- inf, max_value=inf, verbose=0, random_state=None, add_indicator=False) [source]
... | sklearn.modules.generated.sklearn.impute.iterativeimputer#sklearn.impute.IterativeImputer |
sklearn.impute.IterativeImputer
class sklearn.impute.IterativeImputer(estimator=None, *, missing_values=nan, sample_posterior=False, max_iter=10, tol=0.001, n_nearest_features=None, initial_strategy='mean', imputation_order='ascending', skip_complete=False, min_value=- inf, max_value=inf, verbose=0, random_state=None... | sklearn.modules.generated.sklearn.impute.iterativeimputer |
fit(X, y=None) [source]
Fits the imputer on X and return self. Parameters
Xarray-like, shape (n_samples, n_features)
Input data, where “n_samples” is the number of samples and “n_features” is the number of features.
yignored
Returns
selfobject
Returns self. | sklearn.modules.generated.sklearn.impute.iterativeimputer#sklearn.impute.IterativeImputer.fit |
fit_transform(X, y=None) [source]
Fits the imputer on X and return the transformed X. Parameters
Xarray-like, shape (n_samples, n_features)
Input data, where “n_samples” is the number of samples and “n_features” is the number of features.
yignored.
Returns
Xtarray-like, shape (n_samples, n_features)
T... | sklearn.modules.generated.sklearn.impute.iterativeimputer#sklearn.impute.IterativeImputer.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.impute.iterativeimputer#sklearn.impute.IterativeImputer.get_params |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.impute.iterativeimputer#sklearn.impute.IterativeImputer.set_params |
transform(X) [source]
Imputes all missing values in X. Note that this is stochastic, and that if random_state is not fixed, repeated calls, or permuted input, will yield different results. Parameters
Xarray-like of shape (n_samples, n_features)
The input data to complete. Returns
Xtarray-like, shape (n_sa... | sklearn.modules.generated.sklearn.impute.iterativeimputer#sklearn.impute.IterativeImputer.transform |
class sklearn.impute.KNNImputer(*, missing_values=nan, n_neighbors=5, weights='uniform', metric='nan_euclidean', copy=True, add_indicator=False) [source]
Imputation for completing missing values using k-Nearest Neighbors. Each sample’s missing values are imputed using the mean value from n_neighbors nearest neighbors... | sklearn.modules.generated.sklearn.impute.knnimputer#sklearn.impute.KNNImputer |
sklearn.impute.KNNImputer
class sklearn.impute.KNNImputer(*, missing_values=nan, n_neighbors=5, weights='uniform', metric='nan_euclidean', copy=True, add_indicator=False) [source]
Imputation for completing missing values using k-Nearest Neighbors. Each sample’s missing values are imputed using the mean value from n... | sklearn.modules.generated.sklearn.impute.knnimputer |
fit(X, y=None) [source]
Fit the imputer on X. Parameters
Xarray-like shape of (n_samples, n_features)
Input data, where n_samples is the number of samples and n_features is the number of features. Returns
selfobject | sklearn.modules.generated.sklearn.impute.knnimputer#sklearn.impute.KNNImputer.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.impute.knnimputer#sklearn.impute.KNNImputer.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.impute.knnimputer#sklearn.impute.KNNImputer.get_params |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.impute.knnimputer#sklearn.impute.KNNImputer.set_params |
transform(X) [source]
Impute all missing values in X. Parameters
Xarray-like of shape (n_samples, n_features)
The input data to complete. Returns
Xarray-like of shape (n_samples, n_output_features)
The imputed dataset. n_output_features is the number of features that is not always missing during fit. | sklearn.modules.generated.sklearn.impute.knnimputer#sklearn.impute.KNNImputer.transform |
class sklearn.impute.MissingIndicator(*, missing_values=nan, features='missing-only', sparse='auto', error_on_new=True) [source]
Binary indicators for missing values. Note that this component typically should not be used in a vanilla Pipeline consisting of transformers and a classifier, but rather could be added usin... | sklearn.modules.generated.sklearn.impute.missingindicator#sklearn.impute.MissingIndicator |
sklearn.impute.MissingIndicator
class sklearn.impute.MissingIndicator(*, missing_values=nan, features='missing-only', sparse='auto', error_on_new=True) [source]
Binary indicators for missing values. Note that this component typically should not be used in a vanilla Pipeline consisting of transformers and a classifi... | sklearn.modules.generated.sklearn.impute.missingindicator |
fit(X, y=None) [source]
Fit the transformer on X. Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)
Input data, where n_samples is the number of samples and n_features is the number of features. Returns
selfobject
Returns self. | sklearn.modules.generated.sklearn.impute.missingindicator#sklearn.impute.MissingIndicator.fit |
fit_transform(X, y=None) [source]
Generate missing values indicator for X. Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)
The input data to complete. Returns
Xt{ndarray or sparse matrix}, shape (n_samples, n_features) or (n_samples, n_features_with_missing)
The missing indicator ... | sklearn.modules.generated.sklearn.impute.missingindicator#sklearn.impute.MissingIndicator.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.impute.missingindicator#sklearn.impute.MissingIndicator.get_params |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.impute.missingindicator#sklearn.impute.MissingIndicator.set_params |
transform(X) [source]
Generate missing values indicator for X. Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)
The input data to complete. Returns
Xt{ndarray or sparse matrix}, shape (n_samples, n_features) or (n_samples, n_features_with_missing)
The missing indicator for input da... | sklearn.modules.generated.sklearn.impute.missingindicator#sklearn.impute.MissingIndicator.transform |
class sklearn.impute.SimpleImputer(*, missing_values=nan, strategy='mean', fill_value=None, verbose=0, copy=True, add_indicator=False) [source]
Imputation transformer for completing missing values. Read more in the User Guide. New in version 0.20: SimpleImputer replaces the previous sklearn.preprocessing.Imputer est... | sklearn.modules.generated.sklearn.impute.simpleimputer#sklearn.impute.SimpleImputer |
sklearn.impute.SimpleImputer
class sklearn.impute.SimpleImputer(*, missing_values=nan, strategy='mean', fill_value=None, verbose=0, copy=True, add_indicator=False) [source]
Imputation transformer for completing missing values. Read more in the User Guide. New in version 0.20: SimpleImputer replaces the previous sk... | sklearn.modules.generated.sklearn.impute.simpleimputer |
fit(X, y=None) [source]
Fit the imputer on X. Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)
Input data, where n_samples is the number of samples and n_features is the number of features. Returns
selfSimpleImputer | sklearn.modules.generated.sklearn.impute.simpleimputer#sklearn.impute.SimpleImputer.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.impute.simpleimputer#sklearn.impute.SimpleImputer.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.impute.simpleimputer#sklearn.impute.SimpleImputer.get_params |
inverse_transform(X) [source]
Convert the data back to the original representation. Inverts the transform operation performed on an array. This operation can only be performed after SimpleImputer is instantiated with add_indicator=True. Note that inverse_transform can only invert the transform in features that have b... | sklearn.modules.generated.sklearn.impute.simpleimputer#sklearn.impute.SimpleImputer.inverse_transform |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.impute.simpleimputer#sklearn.impute.SimpleImputer.set_params |
transform(X) [source]
Impute all missing values in X. Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)
The input data to complete. | sklearn.modules.generated.sklearn.impute.simpleimputer#sklearn.impute.SimpleImputer.transform |
class sklearn.inspection.PartialDependenceDisplay(pd_results, *, features, feature_names, target_idx, pdp_lim, deciles, kind='average', subsample=1000, random_state=None) [source]
Partial Dependence Plot (PDP). This can also display individual partial dependencies which are often referred to as: Individual Condition ... | sklearn.modules.generated.sklearn.inspection.partialdependencedisplay#sklearn.inspection.PartialDependenceDisplay |
sklearn.inspection.PartialDependenceDisplay
class sklearn.inspection.PartialDependenceDisplay(pd_results, *, features, feature_names, target_idx, pdp_lim, deciles, kind='average', subsample=1000, random_state=None) [source]
Partial Dependence Plot (PDP). This can also display individual partial dependencies which a... | sklearn.modules.generated.sklearn.inspection.partialdependencedisplay |
plot(*, ax=None, n_cols=3, line_kw=None, contour_kw=None) [source]
Plot partial dependence plots. Parameters
axMatplotlib axes or array-like of Matplotlib axes, default=None
If a single axis is passed in, it is treated as a bounding axes
and a grid of partial dependence plots will be drawn within these boun... | sklearn.modules.generated.sklearn.inspection.partialdependencedisplay#sklearn.inspection.PartialDependenceDisplay.plot |
sklearn.inspection.partial_dependence(estimator, X, features, *, response_method='auto', percentiles=0.05, 0.95, grid_resolution=100, method='auto', kind='legacy') [source]
Partial dependence of features. Partial dependence of a feature (or a set of features) corresponds to the average response of an estimator for ea... | sklearn.modules.generated.sklearn.inspection.partial_dependence#sklearn.inspection.partial_dependence |
sklearn.inspection.permutation_importance(estimator, X, y, *, scoring=None, n_repeats=5, n_jobs=None, random_state=None, sample_weight=None) [source]
Permutation importance for feature evaluation [BRE]. The estimator is required to be a fitted estimator. X can be the data set used to train the estimator or a hold-out... | sklearn.modules.generated.sklearn.inspection.permutation_importance#sklearn.inspection.permutation_importance |
sklearn.inspection.plot_partial_dependence(estimator, X, features, *, feature_names=None, target=None, response_method='auto', n_cols=3, grid_resolution=100, percentiles=0.05, 0.95, method='auto', n_jobs=None, verbose=0, line_kw=None, contour_kw=None, ax=None, kind='average', subsample=1000, random_state=None) [source]... | sklearn.modules.generated.sklearn.inspection.plot_partial_dependence#sklearn.inspection.plot_partial_dependence |
sklearn.isotonic.check_increasing(x, y) [source]
Determine whether y is monotonically correlated with x. y is found increasing or decreasing with respect to x based on a Spearman correlation test. Parameters
xarray-like of shape (n_samples,)
Training data.
yarray-like of shape (n_samples,)
Training target. ... | sklearn.modules.generated.sklearn.isotonic.check_increasing#sklearn.isotonic.check_increasing |
class sklearn.isotonic.IsotonicRegression(*, y_min=None, y_max=None, increasing=True, out_of_bounds='nan') [source]
Isotonic regression model. Read more in the User Guide. New in version 0.13. Parameters
y_minfloat, default=None
Lower bound on the lowest predicted value (the minimum value may still be higher)... | sklearn.modules.generated.sklearn.isotonic.isotonicregression#sklearn.isotonic.IsotonicRegression |
sklearn.isotonic.IsotonicRegression
class sklearn.isotonic.IsotonicRegression(*, y_min=None, y_max=None, increasing=True, out_of_bounds='nan') [source]
Isotonic regression model. Read more in the User Guide. New in version 0.13. Parameters
y_minfloat, default=None
Lower bound on the lowest predicted value (... | sklearn.modules.generated.sklearn.isotonic.isotonicregression |
fit(X, y, sample_weight=None) [source]
Fit the model using X, y as training data. Parameters
Xarray-like of shape (n_samples,) or (n_samples, 1)
Training data. Changed in version 0.24: Also accepts 2d array with 1 feature.
yarray-like of shape (n_samples,)
Training target.
sample_weightarray-like of sha... | sklearn.modules.generated.sklearn.isotonic.isotonicregression#sklearn.isotonic.IsotonicRegression.fit |
fit_transform(X, y=None, **fit_params) [source]
Fit to data, then transform it. Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X. Parameters
Xarray-like of shape (n_samples, n_features)
Input samples.
yarray-like of shape (n_samples,) or (n_samples, n_outp... | sklearn.modules.generated.sklearn.isotonic.isotonicregression#sklearn.isotonic.IsotonicRegression.fit_transform |
get_params(deep=True) [source]
Get parameters for this estimator. Parameters
deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators. Returns
paramsdict
Parameter names mapped to their values. | sklearn.modules.generated.sklearn.isotonic.isotonicregression#sklearn.isotonic.IsotonicRegression.get_params |
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