DOC fix links in truncated SVD docs
@@ -27,16 +27,16 @@ class TruncatedSVD(TransformerMixin, BaseEstimator):
This transformer performs linear dimensionality reduction by means of
truncated singular value decomposition (SVD). Contrary to PCA, this
estimator does not center the data before computing the singular value
- decomposition. This means it can work with scipy.sparse matrices
+ decomposition. This means it can work with sparse matrices
efficiently.
In particular, truncated SVD works on term count/tf-idf matrices as
- returned by the vectorizers in sklearn.feature_extraction.text. In that
- context, it is known as latent semantic analysis (LSA).
+ returned by the vectorizers in :mod:`sklearn.feature_extraction.text`. In
+ that context, it is known as latent semantic analysis (LSA).
This estimator supports two algorithms: a fast randomized SVD solver, and
- a "naive" algorithm that uses ARPACK as an eigensolver on (X * X.T) or
- (X.T * X), whichever is more efficient.
+ a "naive" algorithm that uses ARPACK as an eigensolver on `X * X.T` or
+ `X.T * X`, whichever is more efficient.
Read more in the :ref:`User Guide <LSA>`.
@@ -56,8 +56,8 @@ class TruncatedSVD(TransformerMixin, BaseEstimator):
n_iter : int, optional (default 5)
Number of iterations for randomized SVD solver. Not used by ARPACK. The
default is larger than the default in
- `~sklearn.utils.extmath.randomized_svd` to handle sparse matrices that
- may have large slowly decaying spectrum.
+ :func:`~sklearn.utils.extmath.randomized_svd` to handle sparse
+ matrices that may have large slowly decaying spectrum.
random_state : int, RandomState instance, default=None
Used during randomized svd. Pass an int for reproducible results across
