doc_content stringlengths 1 386k | doc_id stringlengths 5 188 |
|---|---|
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_extraction.text.countvectorizer#sklearn.feature_extraction.text.CountVectorizer.get_params |
get_stop_words() [source]
Build or fetch the effective stop words list. Returns
stop_words: list or None
A list of stop words. | sklearn.modules.generated.sklearn.feature_extraction.text.countvectorizer#sklearn.feature_extraction.text.CountVectorizer.get_stop_words |
inverse_transform(X) [source]
Return terms per document with nonzero entries in X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Document-term matrix. Returns
X_invlist of arrays of shape (n_samples,)
List of arrays of terms. | sklearn.modules.generated.sklearn.feature_extraction.text.countvectorizer#sklearn.feature_extraction.text.CountVectorizer.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_extraction.text.countvectorizer#sklearn.feature_extraction.text.CountVectorizer.set_params |
transform(raw_documents) [source]
Transform documents to document-term matrix. Extract token counts out of raw text documents using the vocabulary fitted with fit or the one provided to the constructor. Parameters
raw_documentsiterable
An iterable which yields either str, unicode or file objects. Returns
... | sklearn.modules.generated.sklearn.feature_extraction.text.countvectorizer#sklearn.feature_extraction.text.CountVectorizer.transform |
class sklearn.feature_extraction.text.HashingVectorizer(*, input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern='(?u)\\b\\w\\w+\\b', ngram_range=(1, 1), analyzer='word', n_features=1048576, binary=False, norm='l2'... | sklearn.modules.generated.sklearn.feature_extraction.text.hashingvectorizer#sklearn.feature_extraction.text.HashingVectorizer |
sklearn.feature_extraction.text.HashingVectorizer
class sklearn.feature_extraction.text.HashingVectorizer(*, input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern='(?u)\\b\\w\\w+\\b', ngram_range=(1, 1), analyzer... | sklearn.modules.generated.sklearn.feature_extraction.text.hashingvectorizer |
build_analyzer() [source]
Return a callable that handles preprocessing, tokenization and n-grams generation. Returns
analyzer: callable
A function to handle preprocessing, tokenization and n-grams generation. | sklearn.modules.generated.sklearn.feature_extraction.text.hashingvectorizer#sklearn.feature_extraction.text.HashingVectorizer.build_analyzer |
build_preprocessor() [source]
Return a function to preprocess the text before tokenization. Returns
preprocessor: callable
A function to preprocess the text before tokenization. | sklearn.modules.generated.sklearn.feature_extraction.text.hashingvectorizer#sklearn.feature_extraction.text.HashingVectorizer.build_preprocessor |
build_tokenizer() [source]
Return a function that splits a string into a sequence of tokens. Returns
tokenizer: callable
A function to split a string into a sequence of tokens. | sklearn.modules.generated.sklearn.feature_extraction.text.hashingvectorizer#sklearn.feature_extraction.text.HashingVectorizer.build_tokenizer |
decode(doc) [source]
Decode the input into a string of unicode symbols. The decoding strategy depends on the vectorizer parameters. Parameters
docstr
The string to decode. Returns
doc: str
A string of unicode symbols. | sklearn.modules.generated.sklearn.feature_extraction.text.hashingvectorizer#sklearn.feature_extraction.text.HashingVectorizer.decode |
fit(X, y=None) [source]
Does nothing: this transformer is stateless. Parameters
Xndarray of shape [n_samples, n_features]
Training data. | sklearn.modules.generated.sklearn.feature_extraction.text.hashingvectorizer#sklearn.feature_extraction.text.HashingVectorizer.fit |
fit_transform(X, y=None) [source]
Transform a sequence of documents to a document-term matrix. Parameters
Xiterable over raw text documents, length = n_samples
Samples. Each sample must be a text document (either bytes or unicode strings, file name or file object depending on the constructor argument) which wil... | sklearn.modules.generated.sklearn.feature_extraction.text.hashingvectorizer#sklearn.feature_extraction.text.HashingVectorizer.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_extraction.text.hashingvectorizer#sklearn.feature_extraction.text.HashingVectorizer.get_params |
get_stop_words() [source]
Build or fetch the effective stop words list. Returns
stop_words: list or None
A list of stop words. | sklearn.modules.generated.sklearn.feature_extraction.text.hashingvectorizer#sklearn.feature_extraction.text.HashingVectorizer.get_stop_words |
partial_fit(X, y=None) [source]
Does nothing: this transformer is stateless. This method is just there to mark the fact that this transformer can work in a streaming setup. Parameters
Xndarray of shape [n_samples, n_features]
Training data. | sklearn.modules.generated.sklearn.feature_extraction.text.hashingvectorizer#sklearn.feature_extraction.text.HashingVectorizer.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_extraction.text.hashingvectorizer#sklearn.feature_extraction.text.HashingVectorizer.set_params |
transform(X) [source]
Transform a sequence of documents to a document-term matrix. Parameters
Xiterable over raw text documents, length = n_samples
Samples. Each sample must be a text document (either bytes or unicode strings, file name or file object depending on the constructor argument) which will be tokeniz... | sklearn.modules.generated.sklearn.feature_extraction.text.hashingvectorizer#sklearn.feature_extraction.text.HashingVectorizer.transform |
class sklearn.feature_extraction.text.TfidfTransformer(*, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False) [source]
Transform a count matrix to a normalized tf or tf-idf representation Tf means term-frequency while tf-idf means term-frequency times inverse document-frequency. This is a common term weight... | sklearn.modules.generated.sklearn.feature_extraction.text.tfidftransformer#sklearn.feature_extraction.text.TfidfTransformer |
sklearn.feature_extraction.text.TfidfTransformer
class sklearn.feature_extraction.text.TfidfTransformer(*, norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False) [source]
Transform a count matrix to a normalized tf or tf-idf representation Tf means term-frequency while tf-idf means term-frequency times inver... | sklearn.modules.generated.sklearn.feature_extraction.text.tfidftransformer |
fit(X, y=None) [source]
Learn the idf vector (global term weights). Parameters
Xsparse matrix of shape n_samples, n_features)
A matrix of term/token counts. | sklearn.modules.generated.sklearn.feature_extraction.text.tfidftransformer#sklearn.feature_extraction.text.TfidfTransformer.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_extraction.text.tfidftransformer#sklearn.feature_extraction.text.TfidfTransformer.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_extraction.text.tfidftransformer#sklearn.feature_extraction.text.TfidfTransformer.get_params |
set_params(**params) [source]
Set the parameters of this estimator. The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object. Parameters
**paramsdict
Es... | sklearn.modules.generated.sklearn.feature_extraction.text.tfidftransformer#sklearn.feature_extraction.text.TfidfTransformer.set_params |
transform(X, copy=True) [source]
Transform a count matrix to a tf or tf-idf representation Parameters
Xsparse matrix of (n_samples, n_features)
a matrix of term/token counts
copybool, default=True
Whether to copy X and operate on the copy or perform in-place operations. Returns
vectorssparse matrix of... | sklearn.modules.generated.sklearn.feature_extraction.text.tfidftransformer#sklearn.feature_extraction.text.TfidfTransformer.transform |
class sklearn.feature_extraction.text.TfidfVectorizer(*, input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, analyzer='word', stop_words=None, token_pattern='(?u)\\b\\w\\w+\\b', ngram_range=(1, 1), max_df=1.0, min_df=1, max_features=None, voca... | sklearn.modules.generated.sklearn.feature_extraction.text.tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer |
sklearn.feature_extraction.text.TfidfVectorizer
class sklearn.feature_extraction.text.TfidfVectorizer(*, input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, analyzer='word', stop_words=None, token_pattern='(?u)\\b\\w\\w+\\b', ngram_range=(1,... | sklearn.modules.generated.sklearn.feature_extraction.text.tfidfvectorizer |
build_analyzer() [source]
Return a callable that handles preprocessing, tokenization and n-grams generation. Returns
analyzer: callable
A function to handle preprocessing, tokenization and n-grams generation. | sklearn.modules.generated.sklearn.feature_extraction.text.tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer.build_analyzer |
build_preprocessor() [source]
Return a function to preprocess the text before tokenization. Returns
preprocessor: callable
A function to preprocess the text before tokenization. | sklearn.modules.generated.sklearn.feature_extraction.text.tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer.build_preprocessor |
build_tokenizer() [source]
Return a function that splits a string into a sequence of tokens. Returns
tokenizer: callable
A function to split a string into a sequence of tokens. | sklearn.modules.generated.sklearn.feature_extraction.text.tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer.build_tokenizer |
decode(doc) [source]
Decode the input into a string of unicode symbols. The decoding strategy depends on the vectorizer parameters. Parameters
docstr
The string to decode. Returns
doc: str
A string of unicode symbols. | sklearn.modules.generated.sklearn.feature_extraction.text.tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer.decode |
fit(raw_documents, y=None) [source]
Learn vocabulary and idf from training set. Parameters
raw_documentsiterable
An iterable which yields either str, unicode or file objects.
yNone
This parameter is not needed to compute tfidf. Returns
selfobject
Fitted vectorizer. | sklearn.modules.generated.sklearn.feature_extraction.text.tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer.fit |
fit_transform(raw_documents, y=None) [source]
Learn vocabulary and idf, return document-term matrix. This is equivalent to fit followed by transform, but more efficiently implemented. Parameters
raw_documentsiterable
An iterable which yields either str, unicode or file objects.
yNone
This parameter is ignor... | sklearn.modules.generated.sklearn.feature_extraction.text.tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer.fit_transform |
get_feature_names() [source]
Array mapping from feature integer indices to feature name. Returns
feature_nameslist
A list of feature names. | sklearn.modules.generated.sklearn.feature_extraction.text.tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer.get_feature_names |
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_extraction.text.tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer.get_params |
get_stop_words() [source]
Build or fetch the effective stop words list. Returns
stop_words: list or None
A list of stop words. | sklearn.modules.generated.sklearn.feature_extraction.text.tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer.get_stop_words |
inverse_transform(X) [source]
Return terms per document with nonzero entries in X. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Document-term matrix. Returns
X_invlist of arrays of shape (n_samples,)
List of arrays of terms. | sklearn.modules.generated.sklearn.feature_extraction.text.tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer.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_extraction.text.tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer.set_params |
transform(raw_documents) [source]
Transform documents to document-term matrix. Uses the vocabulary and document frequencies (df) learned by fit (or fit_transform). Parameters
raw_documentsiterable
An iterable which yields either str, unicode or file objects. Returns
Xsparse matrix of (n_samples, n_feature... | sklearn.modules.generated.sklearn.feature_extraction.text.tfidfvectorizer#sklearn.feature_extraction.text.TfidfVectorizer.transform |
sklearn.feature_selection.chi2(X, y) [source]
Compute chi-squared stats between each non-negative feature and class. This score can be used to select the n_features features with the highest values for the test chi-squared statistic from X, which must contain only non-negative features such as booleans or frequencies... | sklearn.modules.generated.sklearn.feature_selection.chi2#sklearn.feature_selection.chi2 |
sklearn.feature_selection.f_classif(X, y) [source]
Compute the ANOVA F-value for the provided sample. Read more in the User Guide. Parameters
X{array-like, sparse matrix} shape = [n_samples, n_features]
The set of regressors that will be tested sequentially.
yarray of shape(n_samples)
The data matrix. Re... | sklearn.modules.generated.sklearn.feature_selection.f_classif#sklearn.feature_selection.f_classif |
sklearn.feature_selection.f_regression(X, y, *, center=True) [source]
Univariate linear regression tests. Linear model for testing the individual effect of each of many regressors. This is a scoring function to be used in a feature selection procedure, not a free standing feature selection procedure. This is done in ... | sklearn.modules.generated.sklearn.feature_selection.f_regression#sklearn.feature_selection.f_regression |
class sklearn.feature_selection.GenericUnivariateSelect(score_func=<function f_classif>, *, mode='percentile', param=1e-05) [source]
Univariate feature selector with configurable strategy. Read more in the User Guide. Parameters
score_funccallable, default=f_classif
Function taking two arrays X and y, and retur... | sklearn.modules.generated.sklearn.feature_selection.genericunivariateselect#sklearn.feature_selection.GenericUnivariateSelect |
sklearn.feature_selection.GenericUnivariateSelect
class sklearn.feature_selection.GenericUnivariateSelect(score_func=<function f_classif>, *, mode='percentile', param=1e-05) [source]
Univariate feature selector with configurable strategy. Read more in the User Guide. Parameters
score_funccallable, default=f_cla... | sklearn.modules.generated.sklearn.feature_selection.genericunivariateselect |
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.genericunivariateselect#sklearn.feature_selection.GenericUnivariateSelect.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.genericunivariateselect#sklearn.feature_selection.GenericUnivariateSelect.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.genericunivariateselect#sklearn.feature_selection.GenericUnivariateSelect.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.genericunivariateselect#sklearn.feature_selection.GenericUnivariateSelect.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.genericunivariateselect#sklearn.feature_selection.GenericUnivariateSelect.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.genericunivariateselect#sklearn.feature_selection.GenericUnivariateSelect.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.genericunivariateselect#sklearn.feature_selection.GenericUnivariateSelect.transform |
sklearn.feature_selection.mutual_info_classif(X, y, *, discrete_features='auto', n_neighbors=3, copy=True, random_state=None) [source]
Estimate mutual information for a discrete target variable. Mutual information (MI) [1] between two random variables is a non-negative value, which measures the dependency between the... | sklearn.modules.generated.sklearn.feature_selection.mutual_info_classif#sklearn.feature_selection.mutual_info_classif |
sklearn.feature_selection.mutual_info_regression(X, y, *, discrete_features='auto', n_neighbors=3, copy=True, random_state=None) [source]
Estimate mutual information for a continuous target variable. Mutual information (MI) [1] between two random variables is a non-negative value, which measures the dependency betwee... | sklearn.modules.generated.sklearn.feature_selection.mutual_info_regression#sklearn.feature_selection.mutual_info_regression |
class sklearn.feature_selection.RFE(estimator, *, n_features_to_select=None, step=1, verbose=0, importance_getter='auto') [source]
Feature ranking with recursive feature elimination. Given an external estimator that assigns weights to features (e.g., the coefficients of a linear model), the goal of recursive feature ... | sklearn.modules.generated.sklearn.feature_selection.rfe#sklearn.feature_selection.RFE |
sklearn.feature_selection.RFE
class sklearn.feature_selection.RFE(estimator, *, n_features_to_select=None, step=1, verbose=0, importance_getter='auto') [source]
Feature ranking with recursive feature elimination. Given an external estimator that assigns weights to features (e.g., the coefficients of a linear model)... | sklearn.modules.generated.sklearn.feature_selection.rfe |
decision_function(X) [source]
Compute the decision function of X. Parameters
X{array-like or sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix. Returns
scorearray, shape = [n_sam... | sklearn.modules.generated.sklearn.feature_selection.rfe#sklearn.feature_selection.RFE.decision_function |
fit(X, y) [source]
Fit the RFE model and then the underlying estimator on the selected
features. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
The training input samples.
yarray-like of shape (n_samples,)
The target values. | sklearn.modules.generated.sklearn.feature_selection.rfe#sklearn.feature_selection.RFE.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.rfe#sklearn.feature_selection.RFE.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.rfe#sklearn.feature_selection.RFE.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.rfe#sklearn.feature_selection.RFE.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.rfe#sklearn.feature_selection.RFE.inverse_transform |
predict(X) [source]
Reduce X to the selected features and then predict using the
underlying estimator. Parameters
Xarray of shape [n_samples, n_features]
The input samples. Returns
yarray of shape [n_samples]
The predicted target values. | sklearn.modules.generated.sklearn.feature_selection.rfe#sklearn.feature_selection.RFE.predict |
predict_log_proba(X) [source]
Predict class log-probabilities for X. Parameters
Xarray of shape [n_samples, n_features]
The input samples. Returns
parray of shape (n_samples, n_classes)
The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute classe... | sklearn.modules.generated.sklearn.feature_selection.rfe#sklearn.feature_selection.RFE.predict_log_proba |
predict_proba(X) [source]
Predict class probabilities for X. Parameters
X{array-like or sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix. Returns
parray of shape (n_samples, n_c... | sklearn.modules.generated.sklearn.feature_selection.rfe#sklearn.feature_selection.RFE.predict_proba |
score(X, y) [source]
Reduce X to the selected features and then return the score of the
underlying estimator. Parameters
Xarray of shape [n_samples, n_features]
The input samples.
yarray of shape [n_samples]
The target values. | sklearn.modules.generated.sklearn.feature_selection.rfe#sklearn.feature_selection.RFE.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.feature_selection.rfe#sklearn.feature_selection.RFE.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.rfe#sklearn.feature_selection.RFE.transform |
class sklearn.feature_selection.RFECV(estimator, *, step=1, min_features_to_select=1, cv=None, scoring=None, verbose=0, n_jobs=None, importance_getter='auto') [source]
Feature ranking with recursive feature elimination and cross-validated selection of the best number of features. See glossary entry for cross-validati... | sklearn.modules.generated.sklearn.feature_selection.rfecv#sklearn.feature_selection.RFECV |
sklearn.feature_selection.RFECV
class sklearn.feature_selection.RFECV(estimator, *, step=1, min_features_to_select=1, cv=None, scoring=None, verbose=0, n_jobs=None, importance_getter='auto') [source]
Feature ranking with recursive feature elimination and cross-validated selection of the best number of features. See... | sklearn.modules.generated.sklearn.feature_selection.rfecv |
decision_function(X) [source]
Compute the decision function of X. Parameters
X{array-like or sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix. Returns
scorearray, shape = [n_sam... | sklearn.modules.generated.sklearn.feature_selection.rfecv#sklearn.feature_selection.RFECV.decision_function |
fit(X, y, groups=None) [source]
Fit the RFE model and automatically tune the number of selected
features. Parameters
X{array-like, sparse matrix} of shape (n_samples, n_features)
Training vector, where n_samples is the number of samples and n_features is the total number of features.
yarray-like of shape ... | sklearn.modules.generated.sklearn.feature_selection.rfecv#sklearn.feature_selection.RFECV.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.rfecv#sklearn.feature_selection.RFECV.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.rfecv#sklearn.feature_selection.RFECV.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.rfecv#sklearn.feature_selection.RFECV.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.rfecv#sklearn.feature_selection.RFECV.inverse_transform |
predict(X) [source]
Reduce X to the selected features and then predict using the
underlying estimator. Parameters
Xarray of shape [n_samples, n_features]
The input samples. Returns
yarray of shape [n_samples]
The predicted target values. | sklearn.modules.generated.sklearn.feature_selection.rfecv#sklearn.feature_selection.RFECV.predict |
predict_log_proba(X) [source]
Predict class log-probabilities for X. Parameters
Xarray of shape [n_samples, n_features]
The input samples. Returns
parray of shape (n_samples, n_classes)
The class log-probabilities of the input samples. The order of the classes corresponds to that in the attribute classe... | sklearn.modules.generated.sklearn.feature_selection.rfecv#sklearn.feature_selection.RFECV.predict_log_proba |
predict_proba(X) [source]
Predict class probabilities for X. Parameters
X{array-like or sparse matrix} of shape (n_samples, n_features)
The input samples. Internally, it will be converted to dtype=np.float32 and if a sparse matrix is provided to a sparse csr_matrix. Returns
parray of shape (n_samples, n_c... | sklearn.modules.generated.sklearn.feature_selection.rfecv#sklearn.feature_selection.RFECV.predict_proba |
score(X, y) [source]
Reduce X to the selected features and then return the score of the
underlying estimator. Parameters
Xarray of shape [n_samples, n_features]
The input samples.
yarray of shape [n_samples]
The target values. | sklearn.modules.generated.sklearn.feature_selection.rfecv#sklearn.feature_selection.RFECV.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.feature_selection.rfecv#sklearn.feature_selection.RFECV.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.rfecv#sklearn.feature_selection.RFECV.transform |
class sklearn.feature_selection.SelectFdr(score_func=<function f_classif>, *, alpha=0.05) [source]
Filter: Select the p-values for an estimated false discovery rate This uses the Benjamini-Hochberg procedure. alpha is an upper bound on the expected false discovery rate. Read more in the User Guide. Parameters
sco... | sklearn.modules.generated.sklearn.feature_selection.selectfdr#sklearn.feature_selection.SelectFdr |
sklearn.feature_selection.SelectFdr
class sklearn.feature_selection.SelectFdr(score_func=<function f_classif>, *, alpha=0.05) [source]
Filter: Select the p-values for an estimated false discovery rate This uses the Benjamini-Hochberg procedure. alpha is an upper bound on the expected false discovery rate. Read more... | sklearn.modules.generated.sklearn.feature_selection.selectfdr |
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.selectfdr#sklearn.feature_selection.SelectFdr.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.selectfdr#sklearn.feature_selection.SelectFdr.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.selectfdr#sklearn.feature_selection.SelectFdr.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.selectfdr#sklearn.feature_selection.SelectFdr.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.selectfdr#sklearn.feature_selection.SelectFdr.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.selectfdr#sklearn.feature_selection.SelectFdr.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.selectfdr#sklearn.feature_selection.SelectFdr.transform |
class sklearn.feature_selection.SelectFpr(score_func=<function f_classif>, *, alpha=0.05) [source]
Filter: Select the pvalues below alpha based on a FPR test. FPR test stands for False Positive Rate test. It controls the total amount of false detections. Read more in the User Guide. Parameters
score_funccallable,... | sklearn.modules.generated.sklearn.feature_selection.selectfpr#sklearn.feature_selection.SelectFpr |
sklearn.feature_selection.SelectFpr
class sklearn.feature_selection.SelectFpr(score_func=<function f_classif>, *, alpha=0.05) [source]
Filter: Select the pvalues below alpha based on a FPR test. FPR test stands for False Positive Rate test. It controls the total amount of false detections. Read more in the User Gui... | sklearn.modules.generated.sklearn.feature_selection.selectfpr |
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.selectfpr#sklearn.feature_selection.SelectFpr.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.selectfpr#sklearn.feature_selection.SelectFpr.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.selectfpr#sklearn.feature_selection.SelectFpr.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.selectfpr#sklearn.feature_selection.SelectFpr.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.selectfpr#sklearn.feature_selection.SelectFpr.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.selectfpr#sklearn.feature_selection.SelectFpr.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.selectfpr#sklearn.feature_selection.SelectFpr.transform |
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 Guide. Parameters
estimatorobject
The ... | sklearn.modules.generated.sklearn.feature_selection.selectfrommodel#sklearn.feature_selection.SelectFromModel |
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