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get_n_splits(X=None, y=None, groups=None) [source]
Returns the number of splitting iterations in the cross-validator Parameters
Xobject
Always ignored, exists for compatibility.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility. Returns
n_split... | sklearn.modules.generated.sklearn.model_selection.shufflesplit#sklearn.model_selection.ShuffleSplit.get_n_splits |
split(X, y=None, groups=None) [source]
Generate indices to split data into training and test set. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
The target variable fo... | sklearn.modules.generated.sklearn.model_selection.shufflesplit#sklearn.model_selection.ShuffleSplit.split |
class sklearn.model_selection.StratifiedKFold(n_splits=5, *, shuffle=False, random_state=None) [source]
Stratified K-Folds cross-validator. Provides train/test indices to split data in train/test sets. This cross-validation object is a variation of KFold that returns stratified folds. The folds are made by preserving... | sklearn.modules.generated.sklearn.model_selection.stratifiedkfold#sklearn.model_selection.StratifiedKFold |
sklearn.model_selection.StratifiedKFold
class sklearn.model_selection.StratifiedKFold(n_splits=5, *, shuffle=False, random_state=None) [source]
Stratified K-Folds cross-validator. Provides train/test indices to split data in train/test sets. This cross-validation object is a variation of KFold that returns stratifi... | sklearn.modules.generated.sklearn.model_selection.stratifiedkfold |
get_n_splits(X=None, y=None, groups=None) [source]
Returns the number of splitting iterations in the cross-validator Parameters
Xobject
Always ignored, exists for compatibility.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility. Returns
n_split... | sklearn.modules.generated.sklearn.model_selection.stratifiedkfold#sklearn.model_selection.StratifiedKFold.get_n_splits |
split(X, y, groups=None) [source]
Generate indices to split data into training and test set. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features. Note that providing y is sufficient to generate the splits and he... | sklearn.modules.generated.sklearn.model_selection.stratifiedkfold#sklearn.model_selection.StratifiedKFold.split |
class sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None) [source]
Stratified ShuffleSplit cross-validator Provides train/test indices to split data in train/test sets. This cross-validation object is a merge of StratifiedKFold and ShuffleSplit, which ret... | sklearn.modules.generated.sklearn.model_selection.stratifiedshufflesplit#sklearn.model_selection.StratifiedShuffleSplit |
sklearn.model_selection.StratifiedShuffleSplit
class sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None) [source]
Stratified ShuffleSplit cross-validator Provides train/test indices to split data in train/test sets. This cross-validation object is a mer... | sklearn.modules.generated.sklearn.model_selection.stratifiedshufflesplit |
get_n_splits(X=None, y=None, groups=None) [source]
Returns the number of splitting iterations in the cross-validator Parameters
Xobject
Always ignored, exists for compatibility.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility. Returns
n_split... | sklearn.modules.generated.sklearn.model_selection.stratifiedshufflesplit#sklearn.model_selection.StratifiedShuffleSplit.get_n_splits |
split(X, y, groups=None) [source]
Generate indices to split data into training and test set. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features. Note that providing y is sufficient to generate the splits and he... | sklearn.modules.generated.sklearn.model_selection.stratifiedshufflesplit#sklearn.model_selection.StratifiedShuffleSplit.split |
class sklearn.model_selection.TimeSeriesSplit(n_splits=5, *, max_train_size=None, test_size=None, gap=0) [source]
Time Series cross-validator Provides train/test indices to split time series data samples that are observed at fixed time intervals, in train/test sets. In each split, test indices must be higher than bef... | sklearn.modules.generated.sklearn.model_selection.timeseriessplit#sklearn.model_selection.TimeSeriesSplit |
sklearn.model_selection.TimeSeriesSplit
class sklearn.model_selection.TimeSeriesSplit(n_splits=5, *, max_train_size=None, test_size=None, gap=0) [source]
Time Series cross-validator Provides train/test indices to split time series data samples that are observed at fixed time intervals, in train/test sets. In each s... | sklearn.modules.generated.sklearn.model_selection.timeseriessplit |
get_n_splits(X=None, y=None, groups=None) [source]
Returns the number of splitting iterations in the cross-validator Parameters
Xobject
Always ignored, exists for compatibility.
yobject
Always ignored, exists for compatibility.
groupsobject
Always ignored, exists for compatibility. Returns
n_split... | sklearn.modules.generated.sklearn.model_selection.timeseriessplit#sklearn.model_selection.TimeSeriesSplit.get_n_splits |
split(X, y=None, groups=None) [source]
Generate indices to split data into training and test set. Parameters
Xarray-like of shape (n_samples, n_features)
Training data, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
Always ignored, exists... | sklearn.modules.generated.sklearn.model_selection.timeseriessplit#sklearn.model_selection.TimeSeriesSplit.split |
sklearn.model_selection.train_test_split(*arrays, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None) [source]
Split arrays or matrices into random train and test subsets Quick utility that wraps input validation and next(ShuffleSplit().split(X, y)) and application to input data into a si... | sklearn.modules.generated.sklearn.model_selection.train_test_split#sklearn.model_selection.train_test_split |
sklearn.model_selection.validation_curve(estimator, X, y, *, param_name, param_range, groups=None, cv=None, scoring=None, n_jobs=None, pre_dispatch='all', verbose=0, error_score=nan, fit_params=None) [source]
Validation curve. Determine training and test scores for varying parameter values. Compute scores for an esti... | sklearn.modules.generated.sklearn.model_selection.validation_curve#sklearn.model_selection.validation_curve |
class sklearn.multiclass.OneVsOneClassifier(estimator, *, n_jobs=None) [source]
One-vs-one multiclass strategy This strategy consists in fitting one classifier per class pair. At prediction time, the class which received the most votes is selected. Since it requires to fit n_classes * (n_classes - 1) / 2 classifiers,... | sklearn.modules.generated.sklearn.multiclass.onevsoneclassifier#sklearn.multiclass.OneVsOneClassifier |
sklearn.multiclass.OneVsOneClassifier
class sklearn.multiclass.OneVsOneClassifier(estimator, *, n_jobs=None) [source]
One-vs-one multiclass strategy This strategy consists in fitting one classifier per class pair. At prediction time, the class which received the most votes is selected. Since it requires to fit n_cl... | sklearn.modules.generated.sklearn.multiclass.onevsoneclassifier |
decision_function(X) [source]
Decision function for the OneVsOneClassifier. The decision values for the samples are computed by adding the normalized sum of pair-wise classification confidence levels to the votes in order to disambiguate between the decision values when the votes for all the classes are equal leading... | sklearn.modules.generated.sklearn.multiclass.onevsoneclassifier#sklearn.multiclass.OneVsOneClassifier.decision_function |
fit(X, y) [source]
Fit underlying estimators. Parameters
X(sparse) array-like of shape (n_samples, n_features)
Data.
yarray-like of shape (n_samples,)
Multi-class targets. Returns
self | sklearn.modules.generated.sklearn.multiclass.onevsoneclassifier#sklearn.multiclass.OneVsOneClassifier.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.multiclass.onevsoneclassifier#sklearn.multiclass.OneVsOneClassifier.get_params |
partial_fit(X, y, classes=None) [source]
Partially fit underlying estimators Should be used when memory is inefficient to train all data. Chunks of data can be passed in several iteration, where the first call should have an array of all target variables. Parameters
X(sparse) array-like of shape (n_samples, n_fea... | sklearn.modules.generated.sklearn.multiclass.onevsoneclassifier#sklearn.multiclass.OneVsOneClassifier.partial_fit |
predict(X) [source]
Estimate the best class label for each sample in X. This is implemented as argmax(decision_function(X), axis=1) which will return the label of the class with most votes by estimators predicting the outcome of a decision for each possible class pair. Parameters
X(sparse) array-like of shape (n_... | sklearn.modules.generated.sklearn.multiclass.onevsoneclassifier#sklearn.multiclass.OneVsOneClassifier.predict |
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.multiclass.onevsoneclassifier#sklearn.multiclass.OneVsOneClassifier.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.multiclass.onevsoneclassifier#sklearn.multiclass.OneVsOneClassifier.set_params |
class sklearn.multiclass.OneVsRestClassifier(estimator, *, n_jobs=None) [source]
One-vs-the-rest (OvR) multiclass strategy. Also known as one-vs-all, this strategy consists in fitting one classifier per class. For each classifier, the class is fitted against all the other classes. In addition to its computational eff... | sklearn.modules.generated.sklearn.multiclass.onevsrestclassifier#sklearn.multiclass.OneVsRestClassifier |
sklearn.multiclass.OneVsRestClassifier
class sklearn.multiclass.OneVsRestClassifier(estimator, *, n_jobs=None) [source]
One-vs-the-rest (OvR) multiclass strategy. Also known as one-vs-all, this strategy consists in fitting one classifier per class. For each classifier, the class is fitted against all the other clas... | sklearn.modules.generated.sklearn.multiclass.onevsrestclassifier |
decision_function(X) [source]
Returns the distance of each sample from the decision boundary for each class. This can only be used with estimators which implement the decision_function method. Parameters
Xarray-like of shape (n_samples, n_features)
Returns
Tarray-like of shape (n_samples, n_classes) or (n_s... | sklearn.modules.generated.sklearn.multiclass.onevsrestclassifier#sklearn.multiclass.OneVsRestClassifier.decision_function |
fit(X, y) [source]
Fit underlying estimators. Parameters
X(sparse) array-like of shape (n_samples, n_features)
Data.
y(sparse) array-like of shape (n_samples,) or (n_samples, n_classes)
Multi-class targets. An indicator matrix turns on multilabel classification. Returns
self | sklearn.modules.generated.sklearn.multiclass.onevsrestclassifier#sklearn.multiclass.OneVsRestClassifier.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.multiclass.onevsrestclassifier#sklearn.multiclass.OneVsRestClassifier.get_params |
property multilabel_
Whether this is a multilabel classifier | sklearn.modules.generated.sklearn.multiclass.onevsrestclassifier#sklearn.multiclass.OneVsRestClassifier.multilabel_ |
partial_fit(X, y, classes=None) [source]
Partially fit underlying estimators Should be used when memory is inefficient to train all data. Chunks of data can be passed in several iteration. Parameters
X(sparse) array-like of shape (n_samples, n_features)
Data.
y(sparse) array-like of shape (n_samples,) or (n_s... | sklearn.modules.generated.sklearn.multiclass.onevsrestclassifier#sklearn.multiclass.OneVsRestClassifier.partial_fit |
predict(X) [source]
Predict multi-class targets using underlying estimators. Parameters
X(sparse) array-like of shape (n_samples, n_features)
Data. Returns
y(sparse) array-like of shape (n_samples,) or (n_samples, n_classes)
Predicted multi-class targets. | sklearn.modules.generated.sklearn.multiclass.onevsrestclassifier#sklearn.multiclass.OneVsRestClassifier.predict |
predict_proba(X) [source]
Probability estimates. The returned estimates for all classes are ordered by label of classes. Note that in the multilabel case, each sample can have any number of labels. This returns the marginal probability that the given sample has the label in question. For example, it is entirely consi... | sklearn.modules.generated.sklearn.multiclass.onevsrestclassifier#sklearn.multiclass.OneVsRestClassifier.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.multiclass.onevsrestclassifier#sklearn.multiclass.OneVsRestClassifier.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.multiclass.onevsrestclassifier#sklearn.multiclass.OneVsRestClassifier.set_params |
class sklearn.multiclass.OutputCodeClassifier(estimator, *, code_size=1.5, random_state=None, n_jobs=None) [source]
(Error-Correcting) Output-Code multiclass strategy Output-code based strategies consist in representing each class with a binary code (an array of 0s and 1s). At fitting time, one binary classifier per ... | sklearn.modules.generated.sklearn.multiclass.outputcodeclassifier#sklearn.multiclass.OutputCodeClassifier |
sklearn.multiclass.OutputCodeClassifier
class sklearn.multiclass.OutputCodeClassifier(estimator, *, code_size=1.5, random_state=None, n_jobs=None) [source]
(Error-Correcting) Output-Code multiclass strategy Output-code based strategies consist in representing each class with a binary code (an array of 0s and 1s). A... | sklearn.modules.generated.sklearn.multiclass.outputcodeclassifier |
fit(X, y) [source]
Fit underlying estimators. Parameters
X(sparse) array-like of shape (n_samples, n_features)
Data.
ynumpy array of shape [n_samples]
Multi-class targets. Returns
self | sklearn.modules.generated.sklearn.multiclass.outputcodeclassifier#sklearn.multiclass.OutputCodeClassifier.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.multiclass.outputcodeclassifier#sklearn.multiclass.OutputCodeClassifier.get_params |
predict(X) [source]
Predict multi-class targets using underlying estimators. Parameters
X(sparse) array-like of shape (n_samples, n_features)
Data. Returns
ynumpy array of shape [n_samples]
Predicted multi-class targets. | sklearn.modules.generated.sklearn.multiclass.outputcodeclassifier#sklearn.multiclass.OutputCodeClassifier.predict |
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.multiclass.outputcodeclassifier#sklearn.multiclass.OutputCodeClassifier.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.multiclass.outputcodeclassifier#sklearn.multiclass.OutputCodeClassifier.set_params |
class sklearn.multioutput.ClassifierChain(base_estimator, *, order=None, cv=None, random_state=None) [source]
A multi-label model that arranges binary classifiers into a chain. Each model makes a prediction in the order specified by the chain using all of the available features provided to the model plus the predicti... | sklearn.modules.generated.sklearn.multioutput.classifierchain#sklearn.multioutput.ClassifierChain |
sklearn.multioutput.ClassifierChain
class sklearn.multioutput.ClassifierChain(base_estimator, *, order=None, cv=None, random_state=None) [source]
A multi-label model that arranges binary classifiers into a chain. Each model makes a prediction in the order specified by the chain using all of the available features p... | sklearn.modules.generated.sklearn.multioutput.classifierchain |
decision_function(X) [source]
Evaluate the decision_function of the models in the chain. Parameters
Xarray-like of shape (n_samples, n_features)
Returns
Y_decisionarray-like of shape (n_samples, n_classes)
Returns the decision function of the sample for each model in the chain. | sklearn.modules.generated.sklearn.multioutput.classifierchain#sklearn.multioutput.ClassifierChain.decision_function |
fit(X, Y) [source]
Fit the model to data matrix X and targets Y. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
Yarray-like of shape (n_samples, n_classes)
The target values. Returns
selfobject | sklearn.modules.generated.sklearn.multioutput.classifierchain#sklearn.multioutput.ClassifierChain.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.multioutput.classifierchain#sklearn.multioutput.ClassifierChain.get_params |
predict(X) [source]
Predict on the data matrix X using the ClassifierChain model. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data. Returns
Y_predarray-like of shape (n_samples, n_classes)
The predicted values. | sklearn.modules.generated.sklearn.multioutput.classifierchain#sklearn.multioutput.ClassifierChain.predict |
predict_proba(X) [source]
Predict probability estimates. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Returns
Y_probarray-like of shape (n_samples, n_classes) | sklearn.modules.generated.sklearn.multioutput.classifierchain#sklearn.multioutput.ClassifierChain.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.multioutput.classifierchain#sklearn.multioutput.ClassifierChain.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.multioutput.classifierchain#sklearn.multioutput.ClassifierChain.set_params |
class sklearn.multioutput.MultiOutputClassifier(estimator, *, n_jobs=None) [source]
Multi target classification This strategy consists of fitting one classifier per target. This is a simple strategy for extending classifiers that do not natively support multi-target classification Parameters
estimatorestimator ob... | sklearn.modules.generated.sklearn.multioutput.multioutputclassifier#sklearn.multioutput.MultiOutputClassifier |
sklearn.multioutput.MultiOutputClassifier
class sklearn.multioutput.MultiOutputClassifier(estimator, *, n_jobs=None) [source]
Multi target classification This strategy consists of fitting one classifier per target. This is a simple strategy for extending classifiers that do not natively support multi-target classif... | sklearn.modules.generated.sklearn.multioutput.multioutputclassifier |
fit(X, Y, sample_weight=None, **fit_params) [source]
Fit the model to data matrix X and targets Y. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
Yarray-like of shape (n_samples, n_classes)
The target values.
sample_weightarray-like of shape (n_samples,), default=... | sklearn.modules.generated.sklearn.multioutput.multioutputclassifier#sklearn.multioutput.MultiOutputClassifier.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.multioutput.multioutputclassifier#sklearn.multioutput.MultiOutputClassifier.get_params |
partial_fit(X, y, classes=None, sample_weight=None) [source]
Incrementally fit the model to data. Fit a separate model for each output variable. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Data.
y{array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets. ... | sklearn.modules.generated.sklearn.multioutput.multioutputclassifier#sklearn.multioutput.MultiOutputClassifier.partial_fit |
predict(X) [source]
Predict multi-output variable using a model
trained for each target variable. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Data. Returns
y{array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets predicted across multiple predict... | sklearn.modules.generated.sklearn.multioutput.multioutputclassifier#sklearn.multioutput.MultiOutputClassifier.predict |
property predict_proba
Probability estimates. Returns prediction probabilities for each class of each output. This method will raise a ValueError if any of the estimators do not have predict_proba. Parameters
Xarray-like of shape (n_samples, n_features)
Data Returns
parray of shape (n_samples, n_classes),... | sklearn.modules.generated.sklearn.multioutput.multioutputclassifier#sklearn.multioutput.MultiOutputClassifier.predict_proba |
score(X, y) [source]
Returns the mean accuracy on the given test data and labels. Parameters
Xarray-like of shape (n_samples, n_features)
Test samples
yarray-like of shape (n_samples, n_outputs)
True values for X Returns
scoresfloat
accuracy_score of self.predict(X) versus y | sklearn.modules.generated.sklearn.multioutput.multioutputclassifier#sklearn.multioutput.MultiOutputClassifier.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.multioutput.multioutputclassifier#sklearn.multioutput.MultiOutputClassifier.set_params |
class sklearn.multioutput.MultiOutputRegressor(estimator, *, n_jobs=None) [source]
Multi target regression This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression. New in version 0.18. Parameters
estimato... | sklearn.modules.generated.sklearn.multioutput.multioutputregressor#sklearn.multioutput.MultiOutputRegressor |
sklearn.multioutput.MultiOutputRegressor
class sklearn.multioutput.MultiOutputRegressor(estimator, *, n_jobs=None) [source]
Multi target regression This strategy consists of fitting one regressor per target. This is a simple strategy for extending regressors that do not natively support multi-target regression. Ne... | sklearn.modules.generated.sklearn.multioutput.multioutputregressor |
fit(X, y, sample_weight=None, **fit_params) [source]
Fit the model to data. Fit a separate model for each output variable. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Data.
y{array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets. An indicator matrix tur... | sklearn.modules.generated.sklearn.multioutput.multioutputregressor#sklearn.multioutput.MultiOutputRegressor.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.multioutput.multioutputregressor#sklearn.multioutput.MultiOutputRegressor.get_params |
partial_fit(X, y, sample_weight=None) [source]
Incrementally fit the model to data. Fit a separate model for each output variable. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Data.
y{array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets.
sample_weight... | sklearn.modules.generated.sklearn.multioutput.multioutputregressor#sklearn.multioutput.MultiOutputRegressor.partial_fit |
predict(X) [source]
Predict multi-output variable using a model
trained for each target variable. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Data. Returns
y{array-like, sparse matrix} of shape (n_samples, n_outputs)
Multi-output targets predicted across multiple predict... | sklearn.modules.generated.sklearn.multioutput.multioutputregressor#sklearn.multioutput.MultiOutputRegressor.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.multioutput.multioutputregressor#sklearn.multioutput.MultiOutputRegressor.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.multioutput.multioutputregressor#sklearn.multioutput.MultiOutputRegressor.set_params |
class sklearn.multioutput.RegressorChain(base_estimator, *, order=None, cv=None, random_state=None) [source]
A multi-label model that arranges regressions into a chain. Each model makes a prediction in the order specified by the chain using all of the available features provided to the model plus the predictions of m... | sklearn.modules.generated.sklearn.multioutput.regressorchain#sklearn.multioutput.RegressorChain |
sklearn.multioutput.RegressorChain
class sklearn.multioutput.RegressorChain(base_estimator, *, order=None, cv=None, random_state=None) [source]
A multi-label model that arranges regressions into a chain. Each model makes a prediction in the order specified by the chain using all of the available features provided t... | sklearn.modules.generated.sklearn.multioutput.regressorchain |
fit(X, Y, **fit_params) [source]
Fit the model to data matrix X and targets Y. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data.
Yarray-like of shape (n_samples, n_classes)
The target values.
**fit_paramsdict of string -> object
Parameters passed to the fit method ... | sklearn.modules.generated.sklearn.multioutput.regressorchain#sklearn.multioutput.RegressorChain.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.multioutput.regressorchain#sklearn.multioutput.RegressorChain.get_params |
predict(X) [source]
Predict on the data matrix X using the ClassifierChain model. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The input data. Returns
Y_predarray-like of shape (n_samples, n_classes)
The predicted values. | sklearn.modules.generated.sklearn.multioutput.regressorchain#sklearn.multioutput.RegressorChain.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.multioutput.regressorchain#sklearn.multioutput.RegressorChain.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.multioutput.regressorchain#sklearn.multioutput.RegressorChain.set_params |
class sklearn.naive_bayes.BernoulliNB(*, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None) [source]
Naive Bayes classifier for multivariate Bernoulli models. Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works with occurrence counts, BernoulliNB... | sklearn.modules.generated.sklearn.naive_bayes.bernoullinb#sklearn.naive_bayes.BernoulliNB |
sklearn.naive_bayes.BernoulliNB
class sklearn.naive_bayes.BernoulliNB(*, alpha=1.0, binarize=0.0, fit_prior=True, class_prior=None) [source]
Naive Bayes classifier for multivariate Bernoulli models. Like MultinomialNB, this classifier is suitable for discrete data. The difference is that while MultinomialNB works w... | sklearn.modules.generated.sklearn.naive_bayes.bernoullinb |
fit(X, y, sample_weight=None) [source]
Fit Naive Bayes classifier according to X, y Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
Target values. ... | sklearn.modules.generated.sklearn.naive_bayes.bernoullinb#sklearn.naive_bayes.BernoulliNB.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.naive_bayes.bernoullinb#sklearn.naive_bayes.BernoulliNB.get_params |
partial_fit(X, y, classes=None, sample_weight=None) [source]
Incremental fit on a batch of samples. This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning. This is especially useful when the whole dataset is too big to fit in... | sklearn.modules.generated.sklearn.naive_bayes.bernoullinb#sklearn.naive_bayes.BernoulliNB.partial_fit |
predict(X) [source]
Perform classification on an array of test vectors X. Parameters
Xarray-like of shape (n_samples, n_features)
Returns
Cndarray of shape (n_samples,)
Predicted target values for X | sklearn.modules.generated.sklearn.naive_bayes.bernoullinb#sklearn.naive_bayes.BernoulliNB.predict |
predict_log_proba(X) [source]
Return log-probability estimates for the test vector X. Parameters
Xarray-like of shape (n_samples, n_features)
Returns
Carray-like of shape (n_samples, n_classes)
Returns the log-probability of the samples for each class in the model. The columns correspond to the classes in... | sklearn.modules.generated.sklearn.naive_bayes.bernoullinb#sklearn.naive_bayes.BernoulliNB.predict_log_proba |
predict_proba(X) [source]
Return probability estimates for the test vector X. Parameters
Xarray-like of shape (n_samples, n_features)
Returns
Carray-like of shape (n_samples, n_classes)
Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted orde... | sklearn.modules.generated.sklearn.naive_bayes.bernoullinb#sklearn.naive_bayes.BernoulliNB.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.naive_bayes.bernoullinb#sklearn.naive_bayes.BernoulliNB.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.naive_bayes.bernoullinb#sklearn.naive_bayes.BernoulliNB.set_params |
class sklearn.naive_bayes.CategoricalNB(*, alpha=1.0, fit_prior=True, class_prior=None, min_categories=None) [source]
Naive Bayes classifier for categorical features The categorical Naive Bayes classifier is suitable for classification with discrete features that are categorically distributed. The categories of each ... | sklearn.modules.generated.sklearn.naive_bayes.categoricalnb#sklearn.naive_bayes.CategoricalNB |
sklearn.naive_bayes.CategoricalNB
class sklearn.naive_bayes.CategoricalNB(*, alpha=1.0, fit_prior=True, class_prior=None, min_categories=None) [source]
Naive Bayes classifier for categorical features The categorical Naive Bayes classifier is suitable for classification with discrete features that are categorically ... | sklearn.modules.generated.sklearn.naive_bayes.categoricalnb |
fit(X, y, sample_weight=None) [source]
Fit Naive Bayes classifier according to X, y Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features. Here, each feature of X is assumed to be from a differ... | sklearn.modules.generated.sklearn.naive_bayes.categoricalnb#sklearn.naive_bayes.CategoricalNB.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.naive_bayes.categoricalnb#sklearn.naive_bayes.CategoricalNB.get_params |
partial_fit(X, y, classes=None, sample_weight=None) [source]
Incremental fit on a batch of samples. This method is expected to be called several times consecutively on different chunks of a dataset so as to implement out-of-core or online learning. This is especially useful when the whole dataset is too big to fit in... | sklearn.modules.generated.sklearn.naive_bayes.categoricalnb#sklearn.naive_bayes.CategoricalNB.partial_fit |
predict(X) [source]
Perform classification on an array of test vectors X. Parameters
Xarray-like of shape (n_samples, n_features)
Returns
Cndarray of shape (n_samples,)
Predicted target values for X | sklearn.modules.generated.sklearn.naive_bayes.categoricalnb#sklearn.naive_bayes.CategoricalNB.predict |
predict_log_proba(X) [source]
Return log-probability estimates for the test vector X. Parameters
Xarray-like of shape (n_samples, n_features)
Returns
Carray-like of shape (n_samples, n_classes)
Returns the log-probability of the samples for each class in the model. The columns correspond to the classes in... | sklearn.modules.generated.sklearn.naive_bayes.categoricalnb#sklearn.naive_bayes.CategoricalNB.predict_log_proba |
predict_proba(X) [source]
Return probability estimates for the test vector X. Parameters
Xarray-like of shape (n_samples, n_features)
Returns
Carray-like of shape (n_samples, n_classes)
Returns the probability of the samples for each class in the model. The columns correspond to the classes in sorted orde... | sklearn.modules.generated.sklearn.naive_bayes.categoricalnb#sklearn.naive_bayes.CategoricalNB.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.naive_bayes.categoricalnb#sklearn.naive_bayes.CategoricalNB.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.naive_bayes.categoricalnb#sklearn.naive_bayes.CategoricalNB.set_params |
class sklearn.naive_bayes.ComplementNB(*, alpha=1.0, fit_prior=True, class_prior=None, norm=False) [source]
The Complement Naive Bayes classifier described in Rennie et al. (2003). The Complement Naive Bayes classifier was designed to correct the “severe assumptions” made by the standard Multinomial Naive Bayes class... | sklearn.modules.generated.sklearn.naive_bayes.complementnb#sklearn.naive_bayes.ComplementNB |
sklearn.naive_bayes.ComplementNB
class sklearn.naive_bayes.ComplementNB(*, alpha=1.0, fit_prior=True, class_prior=None, norm=False) [source]
The Complement Naive Bayes classifier described in Rennie et al. (2003). The Complement Naive Bayes classifier was designed to correct the “severe assumptions” made by the sta... | sklearn.modules.generated.sklearn.naive_bayes.complementnb |
fit(X, y, sample_weight=None) [source]
Fit Naive Bayes classifier according to X, y Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vectors, where n_samples is the number of samples and n_features is the number of features.
yarray-like of shape (n_samples,)
Target values. ... | sklearn.modules.generated.sklearn.naive_bayes.complementnb#sklearn.naive_bayes.ComplementNB.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.naive_bayes.complementnb#sklearn.naive_bayes.ComplementNB.get_params |
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