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