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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.decomposition.sparsepca#sklearn.decomposition.SparsePCA.set_params |
transform(X) [source]
Least Squares projection of the data onto the sparse components. To avoid instability issues in case the system is under-determined, regularization can be applied (Ridge regression) via the ridge_alpha parameter. Note that Sparse PCA components orthogonality is not enforced as in PCA hence one c... | sklearn.modules.generated.sklearn.decomposition.sparsepca#sklearn.decomposition.SparsePCA.transform |
sklearn.decomposition.sparse_encode(X, dictionary, *, gram=None, cov=None, algorithm='lasso_lars', n_nonzero_coefs=None, alpha=None, copy_cov=True, init=None, max_iter=1000, n_jobs=None, check_input=True, verbose=0, positive=False) [source]
Sparse coding Each row of the result is the solution to a sparse coding probl... | sklearn.modules.generated.sklearn.decomposition.sparse_encode#sklearn.decomposition.sparse_encode |
class sklearn.decomposition.TruncatedSVD(n_components=2, *, algorithm='randomized', n_iter=5, random_state=None, tol=0.0) [source]
Dimensionality reduction using truncated SVD (aka LSA). This transformer performs linear dimensionality reduction by means of truncated singular value decomposition (SVD). Contrary to PCA... | sklearn.modules.generated.sklearn.decomposition.truncatedsvd#sklearn.decomposition.TruncatedSVD |
sklearn.decomposition.TruncatedSVD
class sklearn.decomposition.TruncatedSVD(n_components=2, *, algorithm='randomized', n_iter=5, random_state=None, tol=0.0) [source]
Dimensionality reduction using truncated SVD (aka LSA). This transformer performs linear dimensionality reduction by means of truncated singular value... | sklearn.modules.generated.sklearn.decomposition.truncatedsvd |
fit(X, y=None) [source]
Fit model on training data X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
yIgnored
Returns
selfobject
Returns the transformer object. | sklearn.modules.generated.sklearn.decomposition.truncatedsvd#sklearn.decomposition.TruncatedSVD.fit |
fit_transform(X, y=None) [source]
Fit model to X and perform dimensionality reduction on X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training data.
yIgnored
Returns
X_newndarray of shape (n_samples, n_components)
Reduced version of X. This will always be a dense array. | sklearn.modules.generated.sklearn.decomposition.truncatedsvd#sklearn.decomposition.TruncatedSVD.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.decomposition.truncatedsvd#sklearn.decomposition.TruncatedSVD.get_params |
inverse_transform(X) [source]
Transform X back to its original space. Returns an array X_original whose transform would be X. Parameters
Xarray-like of shape (n_samples, n_components)
New data. Returns
X_originalndarray of shape (n_samples, n_features)
Note that this is always a dense array. | sklearn.modules.generated.sklearn.decomposition.truncatedsvd#sklearn.decomposition.TruncatedSVD.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.decomposition.truncatedsvd#sklearn.decomposition.TruncatedSVD.set_params |
transform(X) [source]
Perform dimensionality reduction on X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
New data. Returns
X_newndarray of shape (n_samples, n_components)
Reduced version of X. This will always be a dense array. | sklearn.modules.generated.sklearn.decomposition.truncatedsvd#sklearn.decomposition.TruncatedSVD.transform |
class sklearn.discriminant_analysis.LinearDiscriminantAnalysis(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001, covariance_estimator=None) [source]
Linear Discriminant Analysis A classifier with a linear decision boundary, generated by fitting class conditional densiti... | sklearn.modules.generated.sklearn.discriminant_analysis.lineardiscriminantanalysis#sklearn.discriminant_analysis.LinearDiscriminantAnalysis |
sklearn.discriminant_analysis.LinearDiscriminantAnalysis
class sklearn.discriminant_analysis.LinearDiscriminantAnalysis(solver='svd', shrinkage=None, priors=None, n_components=None, store_covariance=False, tol=0.0001, covariance_estimator=None) [source]
Linear Discriminant Analysis A classifier with a linear decisi... | sklearn.modules.generated.sklearn.discriminant_analysis.lineardiscriminantanalysis |
decision_function(X) [source]
Apply decision function to an array of samples. The decision function is equal (up to a constant factor) to the log-posterior of the model, i.e. log p(y = k | x). In a binary classification setting this instead corresponds to the difference log p(y = 1 | x) - log p(y = 0 | x). See Mathem... | sklearn.modules.generated.sklearn.discriminant_analysis.lineardiscriminantanalysis#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.decision_function |
fit(X, y) [source]
Fit LinearDiscriminantAnalysis model according to the given
training data and parameters. Changed in version 0.19: store_covariance has been moved to main constructor. Changed in version 0.19: tol has been moved to main constructor. Parameters
Xarray-like of shape (n_samples, n_features... | sklearn.modules.generated.sklearn.discriminant_analysis.lineardiscriminantanalysis#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.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.discriminant_analysis.lineardiscriminantanalysis#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.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.discriminant_analysis.lineardiscriminantanalysis#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.get_params |
predict(X) [source]
Predict class labels for samples in X. Parameters
Xarray-like or sparse matrix, shape (n_samples, n_features)
Samples. Returns
Carray, shape [n_samples]
Predicted class label per sample. | sklearn.modules.generated.sklearn.discriminant_analysis.lineardiscriminantanalysis#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict |
predict_log_proba(X) [source]
Estimate log probability. Parameters
Xarray-like of shape (n_samples, n_features)
Input data. Returns
Cndarray of shape (n_samples, n_classes)
Estimated log probabilities. | sklearn.modules.generated.sklearn.discriminant_analysis.lineardiscriminantanalysis#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.predict_log_proba |
predict_proba(X) [source]
Estimate probability. Parameters
Xarray-like of shape (n_samples, n_features)
Input data. Returns
Cndarray of shape (n_samples, n_classes)
Estimated probabilities. | sklearn.modules.generated.sklearn.discriminant_analysis.lineardiscriminantanalysis#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.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.discriminant_analysis.lineardiscriminantanalysis#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.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.discriminant_analysis.lineardiscriminantanalysis#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.set_params |
transform(X) [source]
Project data to maximize class separation. Parameters
Xarray-like of shape (n_samples, n_features)
Input data. Returns
X_newndarray of shape (n_samples, n_components)
Transformed data. | sklearn.modules.generated.sklearn.discriminant_analysis.lineardiscriminantanalysis#sklearn.discriminant_analysis.LinearDiscriminantAnalysis.transform |
class sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis(*, priors=None, reg_param=0.0, store_covariance=False, tol=0.0001) [source]
Quadratic Discriminant Analysis A classifier with a quadratic decision boundary, generated by fitting class conditional densities to the data and using Bayes’ rule. The model f... | sklearn.modules.generated.sklearn.discriminant_analysis.quadraticdiscriminantanalysis#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis |
sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis
class sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis(*, priors=None, reg_param=0.0, store_covariance=False, tol=0.0001) [source]
Quadratic Discriminant Analysis A classifier with a quadratic decision boundary, generated by fitting class condit... | sklearn.modules.generated.sklearn.discriminant_analysis.quadraticdiscriminantanalysis |
decision_function(X) [source]
Apply decision function to an array of samples. The decision function is equal (up to a constant factor) to the log-posterior of the model, i.e. log p(y = k | x). In a binary classification setting this instead corresponds to the difference log p(y = 1 | x) - log p(y = 0 | x). See Mathem... | sklearn.modules.generated.sklearn.discriminant_analysis.quadraticdiscriminantanalysis#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.decision_function |
fit(X, y) [source]
Fit the model according to the given training data and parameters. Changed in version 0.19: store_covariances has been moved to main constructor as store_covariance Changed in version 0.19: tol has been moved to main constructor. Parameters
Xarray-like of shape (n_samples, n_features)
Tra... | sklearn.modules.generated.sklearn.discriminant_analysis.quadraticdiscriminantanalysis#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.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.discriminant_analysis.quadraticdiscriminantanalysis#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.get_params |
predict(X) [source]
Perform classification on an array of test vectors X. The predicted class C for each sample in X is returned. Parameters
Xarray-like of shape (n_samples, n_features)
Returns
Cndarray of shape (n_samples,) | sklearn.modules.generated.sklearn.discriminant_analysis.quadraticdiscriminantanalysis#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict |
predict_log_proba(X) [source]
Return log of posterior probabilities of classification. Parameters
Xarray-like of shape (n_samples, n_features)
Array of samples/test vectors. Returns
Cndarray of shape (n_samples, n_classes)
Posterior log-probabilities of classification per class. | sklearn.modules.generated.sklearn.discriminant_analysis.quadraticdiscriminantanalysis#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.predict_log_proba |
predict_proba(X) [source]
Return posterior probabilities of classification. Parameters
Xarray-like of shape (n_samples, n_features)
Array of samples/test vectors. Returns
Cndarray of shape (n_samples, n_classes)
Posterior probabilities of classification per class. | sklearn.modules.generated.sklearn.discriminant_analysis.quadraticdiscriminantanalysis#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.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.discriminant_analysis.quadraticdiscriminantanalysis#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.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.discriminant_analysis.quadraticdiscriminantanalysis#sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis.set_params |
class sklearn.dummy.DummyClassifier(*, strategy='prior', random_state=None, constant=None) [source]
DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real problems. Read more in the User... | sklearn.modules.generated.sklearn.dummy.dummyclassifier#sklearn.dummy.DummyClassifier |
sklearn.dummy.DummyClassifier
class sklearn.dummy.DummyClassifier(*, strategy='prior', random_state=None, constant=None) [source]
DummyClassifier is a classifier that makes predictions using simple rules. This classifier is useful as a simple baseline to compare with other (real) classifiers. Do not use it for real... | sklearn.modules.generated.sklearn.dummy.dummyclassifier |
fit(X, y, sample_weight=None) [source]
Fit the random classifier. Parameters
Xarray-like of shape (n_samples, n_features)
Training data.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
Target values.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
se... | sklearn.modules.generated.sklearn.dummy.dummyclassifier#sklearn.dummy.DummyClassifier.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.dummy.dummyclassifier#sklearn.dummy.DummyClassifier.get_params |
predict(X) [source]
Perform classification on test vectors X. Parameters
Xarray-like of shape (n_samples, n_features)
Test data. Returns
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
Predicted target values for X. | sklearn.modules.generated.sklearn.dummy.dummyclassifier#sklearn.dummy.DummyClassifier.predict |
predict_log_proba(X) [source]
Return log probability estimates for the test vectors X. Parameters
X{array-like, object with finite length or shape}
Training data, requires length = n_samples Returns
Pndarray of shape (n_samples, n_classes) or list of such arrays
Returns the log probability of the sample... | sklearn.modules.generated.sklearn.dummy.dummyclassifier#sklearn.dummy.DummyClassifier.predict_log_proba |
predict_proba(X) [source]
Return probability estimates for the test vectors X. Parameters
Xarray-like of shape (n_samples, n_features)
Test data. Returns
Pndarray of shape (n_samples, n_classes) or list of such arrays
Returns the probability of the sample for each class in the model, where classes are o... | sklearn.modules.generated.sklearn.dummy.dummyclassifier#sklearn.dummy.DummyClassifier.predict_proba |
score(X, y, sample_weight=None) [source]
Returns 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
XNone or array-like of shape (n_samples,... | sklearn.modules.generated.sklearn.dummy.dummyclassifier#sklearn.dummy.DummyClassifier.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.dummy.dummyclassifier#sklearn.dummy.DummyClassifier.set_params |
class sklearn.dummy.DummyRegressor(*, strategy='mean', constant=None, quantile=None) [source]
DummyRegressor is a regressor that makes predictions using simple rules. This regressor is useful as a simple baseline to compare with other (real) regressors. Do not use it for real problems. Read more in the User Guide. N... | sklearn.modules.generated.sklearn.dummy.dummyregressor#sklearn.dummy.DummyRegressor |
sklearn.dummy.DummyRegressor
class sklearn.dummy.DummyRegressor(*, strategy='mean', constant=None, quantile=None) [source]
DummyRegressor is a regressor that makes predictions using simple rules. This regressor is useful as a simple baseline to compare with other (real) regressors. Do not use it for real problems. ... | sklearn.modules.generated.sklearn.dummy.dummyregressor |
fit(X, y, sample_weight=None) [source]
Fit the random regressor. Parameters
Xarray-like of shape (n_samples, n_features)
Training data.
yarray-like of shape (n_samples,) or (n_samples, n_outputs)
Target values.
sample_weightarray-like of shape (n_samples,), default=None
Sample weights. Returns
sel... | sklearn.modules.generated.sklearn.dummy.dummyregressor#sklearn.dummy.DummyRegressor.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.dummy.dummyregressor#sklearn.dummy.DummyRegressor.get_params |
predict(X, return_std=False) [source]
Perform classification on test vectors X. Parameters
Xarray-like of shape (n_samples, n_features)
Test data.
return_stdbool, default=False
Whether to return the standard deviation of posterior prediction. All zeros in this case. New in version 0.20. Returns
yarr... | sklearn.modules.generated.sklearn.dummy.dummyregressor#sklearn.dummy.DummyRegressor.predict |
score(X, y, sample_weight=None) [source]
Returns 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 i... | sklearn.modules.generated.sklearn.dummy.dummyregressor#sklearn.dummy.DummyRegressor.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.dummy.dummyregressor#sklearn.dummy.DummyRegressor.set_params |
class sklearn.ensemble.AdaBoostClassifier(base_estimator=None, *, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None) [source]
An AdaBoost classifier. An AdaBoost [1] classifier is a meta-estimator that begins by fitting a classifier on the original dataset and then fits additional copies of t... | sklearn.modules.generated.sklearn.ensemble.adaboostclassifier#sklearn.ensemble.AdaBoostClassifier |
sklearn.ensemble.AdaBoostClassifier
class sklearn.ensemble.AdaBoostClassifier(base_estimator=None, *, n_estimators=50, learning_rate=1.0, algorithm='SAMME.R', random_state=None) [source]
An AdaBoost classifier. An AdaBoost [1] classifier is a meta-estimator that begins by fitting a classifier on the original datase... | sklearn.modules.generated.sklearn.ensemble.adaboostclassifier |
decision_function(X) [source]
Compute the decision function of X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR. Returns
scorendarray of shape of (n_samples, k)
... | sklearn.modules.generated.sklearn.ensemble.adaboostclassifier#sklearn.ensemble.AdaBoostClassifier.decision_function |
property feature_importances_
The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance. Warning: impurity-based feature importances c... | sklearn.modules.generated.sklearn.ensemble.adaboostclassifier#sklearn.ensemble.AdaBoostClassifier.feature_importances_ |
fit(X, y, sample_weight=None) [source]
Build a boosted classifier from the training set (X, y). Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
yarray-like of shape (n_... | sklearn.modules.generated.sklearn.ensemble.adaboostclassifier#sklearn.ensemble.AdaBoostClassifier.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.ensemble.adaboostclassifier#sklearn.ensemble.AdaBoostClassifier.get_params |
predict(X) [source]
Predict classes for X. The predicted class of an input sample is computed as the weighted mean prediction of the classifiers in the ensemble. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. ... | sklearn.modules.generated.sklearn.ensemble.adaboostclassifier#sklearn.ensemble.AdaBoostClassifier.predict |
predict_log_proba(X) [source]
Predict class log-probabilities for X. The predicted class log-probabilities of an input sample is computed as the weighted mean predicted class log-probabilities of the classifiers in the ensemble. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The traini... | sklearn.modules.generated.sklearn.ensemble.adaboostclassifier#sklearn.ensemble.AdaBoostClassifier.predict_log_proba |
predict_proba(X) [source]
Predict class probabilities for X. The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples... | sklearn.modules.generated.sklearn.ensemble.adaboostclassifier#sklearn.ensemble.AdaBoostClassifier.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.ensemble.adaboostclassifier#sklearn.ensemble.AdaBoostClassifier.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.ensemble.adaboostclassifier#sklearn.ensemble.AdaBoostClassifier.set_params |
staged_decision_function(X) [source]
Compute decision function of X for each boosting iteration. This method allows monitoring (i.e. determine error on testing set) after each boosting iteration. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix c... | sklearn.modules.generated.sklearn.ensemble.adaboostclassifier#sklearn.ensemble.AdaBoostClassifier.staged_decision_function |
staged_predict(X) [source]
Return staged predictions for X. The predicted class of an input sample is computed as the weighted mean prediction of the classifiers in the ensemble. This generator method yields the ensemble prediction after each iteration of boosting and therefore allows monitoring, such as to determine... | sklearn.modules.generated.sklearn.ensemble.adaboostclassifier#sklearn.ensemble.AdaBoostClassifier.staged_predict |
staged_predict_proba(X) [source]
Predict class probabilities for X. The predicted class probabilities of an input sample is computed as the weighted mean predicted class probabilities of the classifiers in the ensemble. This generator method yields the ensemble predicted class probabilities after each iteration of bo... | sklearn.modules.generated.sklearn.ensemble.adaboostclassifier#sklearn.ensemble.AdaBoostClassifier.staged_predict_proba |
staged_score(X, y, sample_weight=None) [source]
Return staged scores for X, y. This generator method yields the ensemble score after each iteration of boosting and therefore allows monitoring, such as to determine the score on a test set after each boost. Parameters
X{array-like, sparse matrix} of shape (n_sample... | sklearn.modules.generated.sklearn.ensemble.adaboostclassifier#sklearn.ensemble.AdaBoostClassifier.staged_score |
class sklearn.ensemble.AdaBoostRegressor(base_estimator=None, *, n_estimators=50, learning_rate=1.0, loss='linear', random_state=None) [source]
An AdaBoost regressor. An AdaBoost [1] regressor is a meta-estimator that begins by fitting a regressor on the original dataset and then fits additional copies of the regress... | sklearn.modules.generated.sklearn.ensemble.adaboostregressor#sklearn.ensemble.AdaBoostRegressor |
sklearn.ensemble.AdaBoostRegressor
class sklearn.ensemble.AdaBoostRegressor(base_estimator=None, *, n_estimators=50, learning_rate=1.0, loss='linear', random_state=None) [source]
An AdaBoost regressor. An AdaBoost [1] regressor is a meta-estimator that begins by fitting a regressor on the original dataset and then ... | sklearn.modules.generated.sklearn.ensemble.adaboostregressor |
property feature_importances_
The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance. Warning: impurity-based feature importances c... | sklearn.modules.generated.sklearn.ensemble.adaboostregressor#sklearn.ensemble.AdaBoostRegressor.feature_importances_ |
fit(X, y, sample_weight=None) [source]
Build a boosted regressor from the training set (X, y). Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, CSR, COO, DOK, or LIL. COO, DOK, and LIL are converted to CSR.
yarray-like of shape (n_s... | sklearn.modules.generated.sklearn.ensemble.adaboostregressor#sklearn.ensemble.AdaBoostRegressor.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.ensemble.adaboostregressor#sklearn.ensemble.AdaBoostRegressor.get_params |
predict(X) [source]
Predict regression value for X. The predicted regression value of an input sample is computed as the weighted median prediction of the classifiers in the ensemble. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrix can be CSC, C... | sklearn.modules.generated.sklearn.ensemble.adaboostregressor#sklearn.ensemble.AdaBoostRegressor.predict |
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.ensemble.adaboostregressor#sklearn.ensemble.AdaBoostRegressor.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.ensemble.adaboostregressor#sklearn.ensemble.AdaBoostRegressor.set_params |
staged_predict(X) [source]
Return staged predictions for X. The predicted regression value of an input sample is computed as the weighted median prediction of the classifiers in the ensemble. This generator method yields the ensemble prediction after each iteration of boosting and therefore allows monitoring, such as... | sklearn.modules.generated.sklearn.ensemble.adaboostregressor#sklearn.ensemble.AdaBoostRegressor.staged_predict |
staged_score(X, y, sample_weight=None) [source]
Return staged scores for X, y. This generator method yields the ensemble score after each iteration of boosting and therefore allows monitoring, such as to determine the score on a test set after each boost. Parameters
X{array-like, sparse matrix} of shape (n_sample... | sklearn.modules.generated.sklearn.ensemble.adaboostregressor#sklearn.ensemble.AdaBoostRegressor.staged_score |
class sklearn.ensemble.BaggingClassifier(base_estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0) [source]
A Bagging classifier. A Bagging classifier is an ensemble meta-estimator... | sklearn.modules.generated.sklearn.ensemble.baggingclassifier#sklearn.ensemble.BaggingClassifier |
sklearn.ensemble.BaggingClassifier
class sklearn.ensemble.BaggingClassifier(base_estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0) [source]
A Bagging classifier. A Bagging cla... | sklearn.modules.generated.sklearn.ensemble.baggingclassifier |
decision_function(X) [source]
Average of the decision functions of the base classifiers. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only if they are supported by the base estimator. Returns
scorendarray of shape (n_samp... | sklearn.modules.generated.sklearn.ensemble.baggingclassifier#sklearn.ensemble.BaggingClassifier.decision_function |
property estimators_samples_
The subset of drawn samples for each base estimator. Returns a dynamically generated list of indices identifying the samples used for fitting each member of the ensemble, i.e., the in-bag samples. Note: the list is re-created at each call to the property in order to reduce the object memo... | sklearn.modules.generated.sklearn.ensemble.baggingclassifier#sklearn.ensemble.BaggingClassifier.estimators_samples_ |
fit(X, y, sample_weight=None) [source]
Build a Bagging ensemble of estimators from the training
set (X, y). Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
yarray-like of sha... | sklearn.modules.generated.sklearn.ensemble.baggingclassifier#sklearn.ensemble.BaggingClassifier.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.ensemble.baggingclassifier#sklearn.ensemble.BaggingClassifier.get_params |
predict(X) [source]
Predict class for X. The predicted class of an input sample is computed as the class with the highest mean predicted probability. If base estimators do not implement a predict_proba method, then it resorts to voting. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Th... | sklearn.modules.generated.sklearn.ensemble.baggingclassifier#sklearn.ensemble.BaggingClassifier.predict |
predict_log_proba(X) [source]
Predict class log-probabilities for X. The predicted class log-probabilities of an input sample is computed as the log of the mean predicted class probabilities of the base estimators in the ensemble. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The trai... | sklearn.modules.generated.sklearn.ensemble.baggingclassifier#sklearn.ensemble.BaggingClassifier.predict_log_proba |
predict_proba(X) [source]
Predict class probabilities for X. The predicted class probabilities of an input sample is computed as the mean predicted class probabilities of the base estimators in the ensemble. If base estimators do not implement a predict_proba method, then it resorts to voting and the predicted class ... | sklearn.modules.generated.sklearn.ensemble.baggingclassifier#sklearn.ensemble.BaggingClassifier.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.ensemble.baggingclassifier#sklearn.ensemble.BaggingClassifier.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.ensemble.baggingclassifier#sklearn.ensemble.BaggingClassifier.set_params |
class sklearn.ensemble.BaggingRegressor(base_estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0) [source]
A Bagging regressor. A Bagging regressor is an ensemble meta-estimator th... | sklearn.modules.generated.sklearn.ensemble.baggingregressor#sklearn.ensemble.BaggingRegressor |
sklearn.ensemble.BaggingRegressor
class sklearn.ensemble.BaggingRegressor(base_estimator=None, n_estimators=10, *, max_samples=1.0, max_features=1.0, bootstrap=True, bootstrap_features=False, oob_score=False, warm_start=False, n_jobs=None, random_state=None, verbose=0) [source]
A Bagging regressor. A Bagging regres... | sklearn.modules.generated.sklearn.ensemble.baggingregressor |
property estimators_samples_
The subset of drawn samples for each base estimator. Returns a dynamically generated list of indices identifying the samples used for fitting each member of the ensemble, i.e., the in-bag samples. Note: the list is re-created at each call to the property in order to reduce the object memo... | sklearn.modules.generated.sklearn.ensemble.baggingregressor#sklearn.ensemble.BaggingRegressor.estimators_samples_ |
fit(X, y, sample_weight=None) [source]
Build a Bagging ensemble of estimators from the training
set (X, y). Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are accepted only if they are supported by the base estimator.
yarray-like of sha... | sklearn.modules.generated.sklearn.ensemble.baggingregressor#sklearn.ensemble.BaggingRegressor.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.ensemble.baggingregressor#sklearn.ensemble.BaggingRegressor.get_params |
predict(X) [source]
Predict regression target for X. The predicted regression target of an input sample is computed as the mean predicted regression targets of the estimators in the ensemble. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Sparse matrices are... | sklearn.modules.generated.sklearn.ensemble.baggingregressor#sklearn.ensemble.BaggingRegressor.predict |
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.ensemble.baggingregressor#sklearn.ensemble.BaggingRegressor.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.ensemble.baggingregressor#sklearn.ensemble.BaggingRegressor.set_params |
class sklearn.ensemble.ExtraTreesClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=False, oob_score=False, n_jobs=None, random_st... | sklearn.modules.generated.sklearn.ensemble.extratreesclassifier#sklearn.ensemble.ExtraTreesClassifier |
sklearn.ensemble.ExtraTreesClassifier
class sklearn.ensemble.ExtraTreesClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=False,... | sklearn.modules.generated.sklearn.ensemble.extratreesclassifier |
apply(X) [source]
Apply trees in the forest to X, return leaf indices. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr_matrix. Return... | sklearn.modules.generated.sklearn.ensemble.extratreesclassifier#sklearn.ensemble.ExtraTreesClassifier.apply |
decision_path(X) [source]
Return the decision path in the forest. New in version 0.18. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into a sparse csr... | sklearn.modules.generated.sklearn.ensemble.extratreesclassifier#sklearn.ensemble.ExtraTreesClassifier.decision_path |
property feature_importances_
The impurity-based feature importances. The higher, the more important the feature. The importance of a feature is computed as the (normalized) total reduction of the criterion brought by that feature. It is also known as the Gini importance. Warning: impurity-based feature importances c... | sklearn.modules.generated.sklearn.ensemble.extratreesclassifier#sklearn.ensemble.ExtraTreesClassifier.feature_importances_ |
fit(X, y, sample_weight=None) [source]
Build a forest of trees from the training set (X, y). Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples. Internally, its dtype will be converted to dtype=np.float32. If a sparse matrix is provided, it will be converted into... | sklearn.modules.generated.sklearn.ensemble.extratreesclassifier#sklearn.ensemble.ExtraTreesClassifier.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.ensemble.extratreesclassifier#sklearn.ensemble.ExtraTreesClassifier.get_params |
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