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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 weighting scheme in information retrieval, that has also found good use in document classification.

The goal of using tf-idf instead of the raw frequencies of occurrence of a token in a given document is to scale down the impact of tokens that occur very frequently in a given corpus and that are hence empirically less informative than features that occur in a small fraction of the training corpus.

The actual formula used for tf-idf is tf * (idf + 1) = tf + tf * idf, instead of tf * idf. The effect of this is that terms with zero idf, i.e. that occur in all documents of a training set, will not be entirely ignored. The formulas used to compute tf and idf depend on parameter settings that correspond to the SMART notation used in IR, as follows:

Tf is “n” (natural) by default, “l” (logarithmic) when sublinear_tf=True. Idf is “t” when use_idf is given, “n” (none) otherwise. Normalization is “c” (cosine) when norm=’l2’, “n” (none) when norm=None.

Read more in the User Guide.


norm : ‘l1’, ‘l2’ or None, optional

Norm used to normalize term vectors. None for no normalization.

use_idf : boolean, default=True

Enable inverse-document-frequency reweighting.

smooth_idf : boolean, default=True

Smooth idf weights by adding one to document frequencies, as if an extra document was seen containing every term in the collection exactly once. Prevents zero divisions.

sublinear_tf : boolean, default=False

Apply sublinear tf scaling, i.e. replace tf with 1 + log(tf).


[Yates2011]R. Baeza-Yates and B. Ribeiro-Neto (2011). Modern Information Retrieval. Addison Wesley, pp. 68-74.
[MRS2008]C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to Information Retrieval. Cambridge University Press, pp. 118-120.


fit(X[, y]) Learn the idf vector (global term weights)
fit_transform(X[, y]) Fit to data, then transform it.
get_params([deep]) Get parameters for this estimator.
set_params(**params) Set the parameters of this estimator.
transform(X[, copy]) Transform a count matrix to a tf or tf-idf representation
__init__(norm='l2', use_idf=True, smooth_idf=True, sublinear_tf=False)[source]
fit(X, y=None)[source]

Learn the idf vector (global term weights)


X : sparse matrix, [n_samples, n_features]

a matrix of term/token counts

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.


X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.


X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.


Get parameters for this estimator.


deep: boolean, optional :

If True, will return the parameters for this estimator and contained subobjects that are estimators.


params : mapping of string to any

Parameter names mapped to their values.


Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:self :
transform(X, copy=True)[source]

Transform a count matrix to a tf or tf-idf representation


X : sparse matrix, [n_samples, n_features]

a matrix of term/token counts

copy : boolean, default True

Whether to copy X and operate on the copy or perform in-place operations.


vectors : sparse matrix, [n_samples, n_features]

Examples using sklearn.feature_extraction.text.TfidfTransformer