17732
12269
sklearn.decomposition._truncated_svd.TruncatedSVD
sklearn.TruncatedSVD
sklearn.decomposition._truncated_svd.TruncatedSVD
1
openml==0.10.2,sklearn==0.22.1
Dimensionality reduction using truncated SVD (aka LSA).
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
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).
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.
2020-05-18T23:44:04
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
algorithm
string
"randomized"
SVD solver to use. Either "arpack" for the ARPACK wrapper in SciPy
(scipy.sparse.linalg.svds), or "randomized" for the randomized
algorithm due to Halko (2009)
n_components
int
1
Desired dimensionality of output data
Must be strictly less than the number of features
The default value is useful for visualisation. For LSA, a value of
100 is recommended
n_iter
int
95
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
random_state
int
42
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`
tol
float
0.0
Tolerance for ARPACK. 0 means machine precision. Ignored by randomized
SVD solver.
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1