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sklearn.feature_selection.SelectFromModel
class sklearn.feature_selection.SelectFromModel(estimator, *, threshold=None, prefit=False, norm_order=1, max_features=None, importance_getter='auto') [source]
Meta-transformer for selecting features based on importance weights. New in version 0.17. Read more in the User ... | sklearn.modules.generated.sklearn.feature_selection.selectfrommodel |
fit(X, y=None, **fit_params) [source]
Fit the SelectFromModel meta-transformer. Parameters
Xarray-like of shape (n_samples, n_features)
The training input samples.
yarray-like of shape (n_samples,), default=None
The target values (integers that correspond to classes in classification, real numbers in regres... | sklearn.modules.generated.sklearn.feature_selection.selectfrommodel#sklearn.feature_selection.SelectFromModel.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.feature_selection.selectfrommodel#sklearn.feature_selection.SelectFromModel.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.feature_selection.selectfrommodel#sklearn.feature_selection.SelectFromModel.get_params |
get_support(indices=False) [source]
Get a mask, or integer index, of the features selected Parameters
indicesbool, default=False
If True, the return value will be an array of integers, rather than a boolean mask. Returns
supportarray
An index that selects the retained features from a feature vector. If ... | sklearn.modules.generated.sklearn.feature_selection.selectfrommodel#sklearn.feature_selection.SelectFromModel.get_support |
inverse_transform(X) [source]
Reverse the transformation operation Parameters
Xarray of shape [n_samples, n_selected_features]
The input samples. Returns
X_rarray of shape [n_samples, n_original_features]
X with columns of zeros inserted where features would have been removed by transform. | sklearn.modules.generated.sklearn.feature_selection.selectfrommodel#sklearn.feature_selection.SelectFromModel.inverse_transform |
partial_fit(X, y=None, **fit_params) [source]
Fit the SelectFromModel meta-transformer only once. Parameters
Xarray-like of shape (n_samples, n_features)
The training input samples.
yarray-like of shape (n_samples,), default=None
The target values (integers that correspond to classes in classification, real... | sklearn.modules.generated.sklearn.feature_selection.selectfrommodel#sklearn.feature_selection.SelectFromModel.partial_fit |
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.feature_selection.selectfrommodel#sklearn.feature_selection.SelectFromModel.set_params |
transform(X) [source]
Reduce X to the selected features. Parameters
Xarray of shape [n_samples, n_features]
The input samples. Returns
X_rarray of shape [n_samples, n_selected_features]
The input samples with only the selected features. | sklearn.modules.generated.sklearn.feature_selection.selectfrommodel#sklearn.feature_selection.SelectFromModel.transform |
class sklearn.feature_selection.SelectFwe(score_func=<function f_classif>, *, alpha=0.05) [source]
Filter: Select the p-values corresponding to Family-wise error rate Read more in the User Guide. Parameters
score_funccallable, default=f_classif
Function taking two arrays X and y, and returning a pair of arrays ... | sklearn.modules.generated.sklearn.feature_selection.selectfwe#sklearn.feature_selection.SelectFwe |
sklearn.feature_selection.SelectFwe
class sklearn.feature_selection.SelectFwe(score_func=<function f_classif>, *, alpha=0.05) [source]
Filter: Select the p-values corresponding to Family-wise error rate Read more in the User Guide. Parameters
score_funccallable, default=f_classif
Function taking two arrays X ... | sklearn.modules.generated.sklearn.feature_selection.selectfwe |
fit(X, y) [source]
Run score function on (X, y) and get the appropriate features. Parameters
Xarray-like of shape (n_samples, n_features)
The training input samples.
yarray-like of shape (n_samples,)
The target values (class labels in classification, real numbers in regression). Returns
selfobject | sklearn.modules.generated.sklearn.feature_selection.selectfwe#sklearn.feature_selection.SelectFwe.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.feature_selection.selectfwe#sklearn.feature_selection.SelectFwe.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.feature_selection.selectfwe#sklearn.feature_selection.SelectFwe.get_params |
get_support(indices=False) [source]
Get a mask, or integer index, of the features selected Parameters
indicesbool, default=False
If True, the return value will be an array of integers, rather than a boolean mask. Returns
supportarray
An index that selects the retained features from a feature vector. If ... | sklearn.modules.generated.sklearn.feature_selection.selectfwe#sklearn.feature_selection.SelectFwe.get_support |
inverse_transform(X) [source]
Reverse the transformation operation Parameters
Xarray of shape [n_samples, n_selected_features]
The input samples. Returns
X_rarray of shape [n_samples, n_original_features]
X with columns of zeros inserted where features would have been removed by transform. | sklearn.modules.generated.sklearn.feature_selection.selectfwe#sklearn.feature_selection.SelectFwe.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.feature_selection.selectfwe#sklearn.feature_selection.SelectFwe.set_params |
transform(X) [source]
Reduce X to the selected features. Parameters
Xarray of shape [n_samples, n_features]
The input samples. Returns
X_rarray of shape [n_samples, n_selected_features]
The input samples with only the selected features. | sklearn.modules.generated.sklearn.feature_selection.selectfwe#sklearn.feature_selection.SelectFwe.transform |
class sklearn.feature_selection.SelectKBest(score_func=<function f_classif>, *, k=10) [source]
Select features according to the k highest scores. Read more in the User Guide. Parameters
score_funccallable, default=f_classif
Function taking two arrays X and y, and returning a pair of arrays (scores, pvalues) or ... | sklearn.modules.generated.sklearn.feature_selection.selectkbest#sklearn.feature_selection.SelectKBest |
sklearn.feature_selection.SelectKBest
class sklearn.feature_selection.SelectKBest(score_func=<function f_classif>, *, k=10) [source]
Select features according to the k highest scores. Read more in the User Guide. Parameters
score_funccallable, default=f_classif
Function taking two arrays X and y, and returnin... | sklearn.modules.generated.sklearn.feature_selection.selectkbest |
fit(X, y) [source]
Run score function on (X, y) and get the appropriate features. Parameters
Xarray-like of shape (n_samples, n_features)
The training input samples.
yarray-like of shape (n_samples,)
The target values (class labels in classification, real numbers in regression). Returns
selfobject | sklearn.modules.generated.sklearn.feature_selection.selectkbest#sklearn.feature_selection.SelectKBest.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.feature_selection.selectkbest#sklearn.feature_selection.SelectKBest.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.feature_selection.selectkbest#sklearn.feature_selection.SelectKBest.get_params |
get_support(indices=False) [source]
Get a mask, or integer index, of the features selected Parameters
indicesbool, default=False
If True, the return value will be an array of integers, rather than a boolean mask. Returns
supportarray
An index that selects the retained features from a feature vector. If ... | sklearn.modules.generated.sklearn.feature_selection.selectkbest#sklearn.feature_selection.SelectKBest.get_support |
inverse_transform(X) [source]
Reverse the transformation operation Parameters
Xarray of shape [n_samples, n_selected_features]
The input samples. Returns
X_rarray of shape [n_samples, n_original_features]
X with columns of zeros inserted where features would have been removed by transform. | sklearn.modules.generated.sklearn.feature_selection.selectkbest#sklearn.feature_selection.SelectKBest.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.feature_selection.selectkbest#sklearn.feature_selection.SelectKBest.set_params |
transform(X) [source]
Reduce X to the selected features. Parameters
Xarray of shape [n_samples, n_features]
The input samples. Returns
X_rarray of shape [n_samples, n_selected_features]
The input samples with only the selected features. | sklearn.modules.generated.sklearn.feature_selection.selectkbest#sklearn.feature_selection.SelectKBest.transform |
class sklearn.feature_selection.SelectorMixin [source]
Transformer mixin that performs feature selection given a support mask This mixin provides a feature selector implementation with transform and inverse_transform functionality given an implementation of _get_support_mask. Methods
fit_transform(X[, y]) Fit to da... | sklearn.modules.generated.sklearn.feature_selection.selectormixin#sklearn.feature_selection.SelectorMixin |
sklearn.feature_selection.SelectorMixin
class sklearn.feature_selection.SelectorMixin [source]
Transformer mixin that performs feature selection given a support mask This mixin provides a feature selector implementation with transform and inverse_transform functionality given an implementation of _get_support_mask.... | sklearn.modules.generated.sklearn.feature_selection.selectormixin |
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.feature_selection.selectormixin#sklearn.feature_selection.SelectorMixin.fit_transform |
get_support(indices=False) [source]
Get a mask, or integer index, of the features selected Parameters
indicesbool, default=False
If True, the return value will be an array of integers, rather than a boolean mask. Returns
supportarray
An index that selects the retained features from a feature vector. If ... | sklearn.modules.generated.sklearn.feature_selection.selectormixin#sklearn.feature_selection.SelectorMixin.get_support |
inverse_transform(X) [source]
Reverse the transformation operation Parameters
Xarray of shape [n_samples, n_selected_features]
The input samples. Returns
X_rarray of shape [n_samples, n_original_features]
X with columns of zeros inserted where features would have been removed by transform. | sklearn.modules.generated.sklearn.feature_selection.selectormixin#sklearn.feature_selection.SelectorMixin.inverse_transform |
transform(X) [source]
Reduce X to the selected features. Parameters
Xarray of shape [n_samples, n_features]
The input samples. Returns
X_rarray of shape [n_samples, n_selected_features]
The input samples with only the selected features. | sklearn.modules.generated.sklearn.feature_selection.selectormixin#sklearn.feature_selection.SelectorMixin.transform |
class sklearn.feature_selection.SelectPercentile(score_func=<function f_classif>, *, percentile=10) [source]
Select features according to a percentile of the highest scores. Read more in the User Guide. Parameters
score_funccallable, default=f_classif
Function taking two arrays X and y, and returning a pair of ... | sklearn.modules.generated.sklearn.feature_selection.selectpercentile#sklearn.feature_selection.SelectPercentile |
sklearn.feature_selection.SelectPercentile
class sklearn.feature_selection.SelectPercentile(score_func=<function f_classif>, *, percentile=10) [source]
Select features according to a percentile of the highest scores. Read more in the User Guide. Parameters
score_funccallable, default=f_classif
Function taking... | sklearn.modules.generated.sklearn.feature_selection.selectpercentile |
fit(X, y) [source]
Run score function on (X, y) and get the appropriate features. Parameters
Xarray-like of shape (n_samples, n_features)
The training input samples.
yarray-like of shape (n_samples,)
The target values (class labels in classification, real numbers in regression). Returns
selfobject | sklearn.modules.generated.sklearn.feature_selection.selectpercentile#sklearn.feature_selection.SelectPercentile.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.feature_selection.selectpercentile#sklearn.feature_selection.SelectPercentile.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.feature_selection.selectpercentile#sklearn.feature_selection.SelectPercentile.get_params |
get_support(indices=False) [source]
Get a mask, or integer index, of the features selected Parameters
indicesbool, default=False
If True, the return value will be an array of integers, rather than a boolean mask. Returns
supportarray
An index that selects the retained features from a feature vector. If ... | sklearn.modules.generated.sklearn.feature_selection.selectpercentile#sklearn.feature_selection.SelectPercentile.get_support |
inverse_transform(X) [source]
Reverse the transformation operation Parameters
Xarray of shape [n_samples, n_selected_features]
The input samples. Returns
X_rarray of shape [n_samples, n_original_features]
X with columns of zeros inserted where features would have been removed by transform. | sklearn.modules.generated.sklearn.feature_selection.selectpercentile#sklearn.feature_selection.SelectPercentile.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.feature_selection.selectpercentile#sklearn.feature_selection.SelectPercentile.set_params |
transform(X) [source]
Reduce X to the selected features. Parameters
Xarray of shape [n_samples, n_features]
The input samples. Returns
X_rarray of shape [n_samples, n_selected_features]
The input samples with only the selected features. | sklearn.modules.generated.sklearn.feature_selection.selectpercentile#sklearn.feature_selection.SelectPercentile.transform |
class sklearn.feature_selection.SequentialFeatureSelector(estimator, *, n_features_to_select=None, direction='forward', scoring=None, cv=5, n_jobs=None) [source]
Transformer that performs Sequential Feature Selection. This Sequential Feature Selector adds (forward selection) or removes (backward selection) features t... | sklearn.modules.generated.sklearn.feature_selection.sequentialfeatureselector#sklearn.feature_selection.SequentialFeatureSelector |
sklearn.feature_selection.SequentialFeatureSelector
class sklearn.feature_selection.SequentialFeatureSelector(estimator, *, n_features_to_select=None, direction='forward', scoring=None, cv=5, n_jobs=None) [source]
Transformer that performs Sequential Feature Selection. This Sequential Feature Selector adds (forward... | sklearn.modules.generated.sklearn.feature_selection.sequentialfeatureselector |
fit(X, y) [source]
Learn the features to select. Parameters
Xarray-like of shape (n_samples, n_features)
Training vectors.
yarray-like of shape (n_samples,)
Target values. Returns
selfobject | sklearn.modules.generated.sklearn.feature_selection.sequentialfeatureselector#sklearn.feature_selection.SequentialFeatureSelector.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.feature_selection.sequentialfeatureselector#sklearn.feature_selection.SequentialFeatureSelector.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.feature_selection.sequentialfeatureselector#sklearn.feature_selection.SequentialFeatureSelector.get_params |
get_support(indices=False) [source]
Get a mask, or integer index, of the features selected Parameters
indicesbool, default=False
If True, the return value will be an array of integers, rather than a boolean mask. Returns
supportarray
An index that selects the retained features from a feature vector. If ... | sklearn.modules.generated.sklearn.feature_selection.sequentialfeatureselector#sklearn.feature_selection.SequentialFeatureSelector.get_support |
inverse_transform(X) [source]
Reverse the transformation operation Parameters
Xarray of shape [n_samples, n_selected_features]
The input samples. Returns
X_rarray of shape [n_samples, n_original_features]
X with columns of zeros inserted where features would have been removed by transform. | sklearn.modules.generated.sklearn.feature_selection.sequentialfeatureselector#sklearn.feature_selection.SequentialFeatureSelector.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.feature_selection.sequentialfeatureselector#sklearn.feature_selection.SequentialFeatureSelector.set_params |
transform(X) [source]
Reduce X to the selected features. Parameters
Xarray of shape [n_samples, n_features]
The input samples. Returns
X_rarray of shape [n_samples, n_selected_features]
The input samples with only the selected features. | sklearn.modules.generated.sklearn.feature_selection.sequentialfeatureselector#sklearn.feature_selection.SequentialFeatureSelector.transform |
class sklearn.feature_selection.VarianceThreshold(threshold=0.0) [source]
Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning. Read more in the User Guide. Parameters
t... | sklearn.modules.generated.sklearn.feature_selection.variancethreshold#sklearn.feature_selection.VarianceThreshold |
sklearn.feature_selection.VarianceThreshold
class sklearn.feature_selection.VarianceThreshold(threshold=0.0) [source]
Feature selector that removes all low-variance features. This feature selection algorithm looks only at the features (X), not the desired outputs (y), and can thus be used for unsupervised learning.... | sklearn.modules.generated.sklearn.feature_selection.variancethreshold |
fit(X, y=None) [source]
Learn empirical variances from X. Parameters
X{array-like, sparse matrix}, shape (n_samples, n_features)
Sample vectors from which to compute variances.
yany, default=None
Ignored. This parameter exists only for compatibility with sklearn.pipeline.Pipeline. Returns
self | sklearn.modules.generated.sklearn.feature_selection.variancethreshold#sklearn.feature_selection.VarianceThreshold.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.feature_selection.variancethreshold#sklearn.feature_selection.VarianceThreshold.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.feature_selection.variancethreshold#sklearn.feature_selection.VarianceThreshold.get_params |
get_support(indices=False) [source]
Get a mask, or integer index, of the features selected Parameters
indicesbool, default=False
If True, the return value will be an array of integers, rather than a boolean mask. Returns
supportarray
An index that selects the retained features from a feature vector. If ... | sklearn.modules.generated.sklearn.feature_selection.variancethreshold#sklearn.feature_selection.VarianceThreshold.get_support |
inverse_transform(X) [source]
Reverse the transformation operation Parameters
Xarray of shape [n_samples, n_selected_features]
The input samples. Returns
X_rarray of shape [n_samples, n_original_features]
X with columns of zeros inserted where features would have been removed by transform. | sklearn.modules.generated.sklearn.feature_selection.variancethreshold#sklearn.feature_selection.VarianceThreshold.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.feature_selection.variancethreshold#sklearn.feature_selection.VarianceThreshold.set_params |
transform(X) [source]
Reduce X to the selected features. Parameters
Xarray of shape [n_samples, n_features]
The input samples. Returns
X_rarray of shape [n_samples, n_selected_features]
The input samples with only the selected features. | sklearn.modules.generated.sklearn.feature_selection.variancethreshold#sklearn.feature_selection.VarianceThreshold.transform |
class sklearn.gaussian_process.GaussianProcessClassifier(kernel=None, *, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0, max_iter_predict=100, warm_start=False, copy_X_train=True, random_state=None, multi_class='one_vs_rest', n_jobs=None) [source]
Gaussian process classification (GPC) based on Laplace approximatio... | sklearn.modules.generated.sklearn.gaussian_process.gaussianprocessclassifier#sklearn.gaussian_process.GaussianProcessClassifier |
sklearn.gaussian_process.GaussianProcessClassifier
class sklearn.gaussian_process.GaussianProcessClassifier(kernel=None, *, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0, max_iter_predict=100, warm_start=False, copy_X_train=True, random_state=None, multi_class='one_vs_rest', n_jobs=None) [source]
Gaussian proce... | sklearn.modules.generated.sklearn.gaussian_process.gaussianprocessclassifier |
fit(X, y) [source]
Fit Gaussian process classification model Parameters
Xarray-like of shape (n_samples, n_features) or list of object
Feature vectors or other representations of training data.
yarray-like of shape (n_samples,)
Target values, must be binary Returns
selfreturns an instance of self. | sklearn.modules.generated.sklearn.gaussian_process.gaussianprocessclassifier#sklearn.gaussian_process.GaussianProcessClassifier.fit |
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.gaussian_process.gaussianprocessclassifier#sklearn.gaussian_process.GaussianProcessClassifier.get_params |
log_marginal_likelihood(theta=None, eval_gradient=False, clone_kernel=True) [source]
Returns log-marginal likelihood of theta for training data. In the case of multi-class classification, the mean log-marginal likelihood of the one-versus-rest classifiers are returned. Parameters
thetaarray-like of shape (n_kerne... | sklearn.modules.generated.sklearn.gaussian_process.gaussianprocessclassifier#sklearn.gaussian_process.GaussianProcessClassifier.log_marginal_likelihood |
predict(X) [source]
Perform classification on an array of test vectors X. Parameters
Xarray-like of shape (n_samples, n_features) or list of object
Query points where the GP is evaluated for classification. Returns
Cndarray of shape (n_samples,)
Predicted target values for X, values are from classes_ | sklearn.modules.generated.sklearn.gaussian_process.gaussianprocessclassifier#sklearn.gaussian_process.GaussianProcessClassifier.predict |
predict_proba(X) [source]
Return probability estimates for the test vector X. Parameters
Xarray-like of shape (n_samples, n_features) or list of object
Query points where the GP is evaluated for classification. Returns
Carray-like of shape (n_samples, n_classes)
Returns the probability of the samples fo... | sklearn.modules.generated.sklearn.gaussian_process.gaussianprocessclassifier#sklearn.gaussian_process.GaussianProcessClassifier.predict_proba |
score(X, y, sample_weight=None) [source]
Return the mean accuracy on the given test data and labels. In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted. Parameters
Xarray-like of shape (n_samples, n_featur... | sklearn.modules.generated.sklearn.gaussian_process.gaussianprocessclassifier#sklearn.gaussian_process.GaussianProcessClassifier.score |
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.gaussian_process.gaussianprocessclassifier#sklearn.gaussian_process.GaussianProcessClassifier.set_params |
class sklearn.gaussian_process.GaussianProcessRegressor(kernel=None, *, alpha=1e-10, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0, normalize_y=False, copy_X_train=True, random_state=None) [source]
Gaussian process regression (GPR). The implementation is based on Algorithm 2.1 of Gaussian Processes for Machine Le... | sklearn.modules.generated.sklearn.gaussian_process.gaussianprocessregressor#sklearn.gaussian_process.GaussianProcessRegressor |
sklearn.gaussian_process.GaussianProcessRegressor
class sklearn.gaussian_process.GaussianProcessRegressor(kernel=None, *, alpha=1e-10, optimizer='fmin_l_bfgs_b', n_restarts_optimizer=0, normalize_y=False, copy_X_train=True, random_state=None) [source]
Gaussian process regression (GPR). The implementation is based o... | sklearn.modules.generated.sklearn.gaussian_process.gaussianprocessregressor |
fit(X, y) [source]
Fit Gaussian process regression model. Parameters
Xarray-like of shape (n_samples, n_features) or list of object
Feature vectors or other representations of training data.
yarray-like of shape (n_samples,) or (n_samples, n_targets)
Target values Returns
selfreturns an instance of se... | sklearn.modules.generated.sklearn.gaussian_process.gaussianprocessregressor#sklearn.gaussian_process.GaussianProcessRegressor.fit |
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.gaussian_process.gaussianprocessregressor#sklearn.gaussian_process.GaussianProcessRegressor.get_params |
log_marginal_likelihood(theta=None, eval_gradient=False, clone_kernel=True) [source]
Returns log-marginal likelihood of theta for training data. Parameters
thetaarray-like of shape (n_kernel_params,) default=None
Kernel hyperparameters for which the log-marginal likelihood is evaluated. If None, the precomputed... | sklearn.modules.generated.sklearn.gaussian_process.gaussianprocessregressor#sklearn.gaussian_process.GaussianProcessRegressor.log_marginal_likelihood |
predict(X, return_std=False, return_cov=False) [source]
Predict using the Gaussian process regression model We can also predict based on an unfitted model by using the GP prior. In addition to the mean of the predictive distribution, also its standard deviation (return_std=True) or covariance (return_cov=True). Note ... | sklearn.modules.generated.sklearn.gaussian_process.gaussianprocessregressor#sklearn.gaussian_process.GaussianProcessRegressor.predict |
sample_y(X, n_samples=1, random_state=0) [source]
Draw samples from Gaussian process and evaluate at X. Parameters
Xarray-like of shape (n_samples, n_features) or list of object
Query points where the GP is evaluated.
n_samplesint, default=1
The number of samples drawn from the Gaussian process
random_sta... | sklearn.modules.generated.sklearn.gaussian_process.gaussianprocessregressor#sklearn.gaussian_process.GaussianProcessRegressor.sample_y |
score(X, y, sample_weight=None) [source]
Return the coefficient of determination \(R^2\) of the prediction. The coefficient \(R^2\) 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()... | sklearn.modules.generated.sklearn.gaussian_process.gaussianprocessregressor#sklearn.gaussian_process.GaussianProcessRegressor.score |
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.gaussian_process.gaussianprocessregressor#sklearn.gaussian_process.GaussianProcessRegressor.set_params |
class sklearn.gaussian_process.kernels.CompoundKernel(kernels) [source]
Kernel which is composed of a set of other kernels. New in version 0.18. Parameters
kernelslist of Kernels
The other kernels Attributes
bounds
Returns the log-transformed bounds on the theta.
hyperparameters
Returns a list of ... | sklearn.modules.generated.sklearn.gaussian_process.kernels.compoundkernel#sklearn.gaussian_process.kernels.CompoundKernel |
sklearn.gaussian_process.kernels.CompoundKernel
class sklearn.gaussian_process.kernels.CompoundKernel(kernels) [source]
Kernel which is composed of a set of other kernels. New in version 0.18. Parameters
kernelslist of Kernels
The other kernels Attributes
bounds
Returns the log-transformed bounds on... | sklearn.modules.generated.sklearn.gaussian_process.kernels.compoundkernel |
property bounds
Returns the log-transformed bounds on the theta. Returns
boundsarray of shape (n_dims, 2)
The log-transformed bounds on the kernel’s hyperparameters theta | sklearn.modules.generated.sklearn.gaussian_process.kernels.compoundkernel#sklearn.gaussian_process.kernels.CompoundKernel.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.compoundkernel#sklearn.gaussian_process.kernels.CompoundKernel.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.compoundkernel#sklearn.gaussian_process.kernels.CompoundKernel.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.compoundkernel#sklearn.gaussian_process.kernels.CompoundKernel.get_params |
property hyperparameters
Returns a list of all hyperparameter specifications. | sklearn.modules.generated.sklearn.gaussian_process.kernels.compoundkernel#sklearn.gaussian_process.kernels.CompoundKernel.hyperparameters |
is_stationary() [source]
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.compoundkernel#sklearn.gaussian_process.kernels.CompoundKernel.is_stationary |
property n_dims
Returns the number of non-fixed hyperparameters of the kernel. | sklearn.modules.generated.sklearn.gaussian_process.kernels.compoundkernel#sklearn.gaussian_process.kernels.CompoundKernel.n_dims |
property requires_vector_input
Returns whether the kernel is defined on discrete structures. | sklearn.modules.generated.sklearn.gaussian_process.kernels.compoundkernel#sklearn.gaussian_process.kernels.CompoundKernel.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.compoundkernel#sklearn.gaussian_process.kernels.CompoundKernel.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.compoundkernel#sklearn.gaussian_process.kernels.CompoundKernel.theta |
__call__(X, Y=None, eval_gradient=False) [source]
Return the kernel k(X, Y) and optionally its gradient. Note that this compound kernel returns the results of all simple kernel stacked along an additional axis. Parameters
Xarray-like of shape (n_samples_X, n_features) or list of object, default=None
Left argume... | sklearn.modules.generated.sklearn.gaussian_process.kernels.compoundkernel#sklearn.gaussian_process.kernels.CompoundKernel.__call__ |
class sklearn.gaussian_process.kernels.ConstantKernel(constant_value=1.0, constant_value_bounds=1e-05, 100000.0) [source]
Constant kernel. Can be used as part of a product-kernel where it scales the magnitude of the other factor (kernel) or as part of a sum-kernel, where it modifies the mean of the Gaussian process. ... | sklearn.modules.generated.sklearn.gaussian_process.kernels.constantkernel#sklearn.gaussian_process.kernels.ConstantKernel |
sklearn.gaussian_process.kernels.ConstantKernel
class sklearn.gaussian_process.kernels.ConstantKernel(constant_value=1.0, constant_value_bounds=1e-05, 100000.0) [source]
Constant kernel. Can be used as part of a product-kernel where it scales the magnitude of the other factor (kernel) or as part of a sum-kernel, wh... | sklearn.modules.generated.sklearn.gaussian_process.kernels.constantkernel |
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.constantkernel#sklearn.gaussian_process.kernels.ConstantKernel.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.constantkernel#sklearn.gaussian_process.kernels.ConstantKernel.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.constantkernel#sklearn.gaussian_process.kernels.ConstantKernel.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.constantkernel#sklearn.gaussian_process.kernels.ConstantKernel.get_params |
property hyperparameters
Returns a list of all hyperparameter specifications. | sklearn.modules.generated.sklearn.gaussian_process.kernels.constantkernel#sklearn.gaussian_process.kernels.ConstantKernel.hyperparameters |
is_stationary() [source]
Returns whether the kernel is stationary. | sklearn.modules.generated.sklearn.gaussian_process.kernels.constantkernel#sklearn.gaussian_process.kernels.ConstantKernel.is_stationary |
property n_dims
Returns the number of non-fixed hyperparameters of the kernel. | sklearn.modules.generated.sklearn.gaussian_process.kernels.constantkernel#sklearn.gaussian_process.kernels.ConstantKernel.n_dims |
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