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[MRG] added class_ to the LogisticRegression documentation #12212

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[MRG] added class_ to the LogisticRegression documentation #12212

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c15d5c8
tfidfvectorizer documentation
Sep 29, 2018
93ae2d1
adding line breaks
Sep 29, 2018
e9974a2
edit class to classes
Sep 29, 2018
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22 changes: 21 additions & 1 deletion sklearn/feature_extraction/text.py
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Expand Up @@ -1286,7 +1286,27 @@ def idf_(self, value):
class TfidfVectorizer(CountVectorizer):
"""Convert a collection of raw documents to a matrix of TF-IDF features.

Equivalent to CountVectorizer followed by TfidfTransformer.
Equivalent to CountVectorizer followed by TfidfTransformer.

CountVectorizer converts a collection of text documents to a matrix of
token counts.

TfidfTransformer then converts the count matrix from CountVectorizer to a
normalized tf-idf representation. Tf is term frequency, and idf is inverse
document frequency. This is a common way to calculate the count of a word
relative to the appearance of a ducument.

The formula that is used to compute the tf-idf of term t is
tf-idf(d, t) = tf(t) * idf(d, t), and the idf is computed as
idf(d, t) = log [ n / df(d, t) ] + 1 (if ``smooth_idf=False``),
where n is the total number of documents and df(d, t) is the
document frequency; the document frequency is the number of documents d
that contain term t. The effect of adding "1" to the idf in the equation
above is that terms with zero idf, i.e., terms that occur in all documents
in a training set, will not be entirely ignored.
(Note that the idf formula above differs from the standard
textbook notation that defines the idf as
idf(d, t) = log [ n / (df(d, t) + 1) ]).

Read more in the :ref:`User Guide <text_feature_extraction>`.

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5 changes: 5 additions & 0 deletions sklearn/linear_model/logistic.py
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Expand Up @@ -1140,6 +1140,11 @@ class LogisticRegression(BaseEstimator, LinearClassifierMixin,

Attributes
----------
classes_ : array or shape (n_classes, )
A list of class labels known to the classifier.

In binary classification, one class is often considered the 'positive'
class. For example, in labels [-1, 1], 1 is the positive class.

coef_ : array, shape (1, n_features) or (n_classes, n_features)
Coefficient of the features in the decision function.
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