Issue | #Downvotes for this reason | By |
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algorithm | default: "randomized" | |
n_components | Desired dimensionality of output data If algorithm='arpack', must be strictly less than the number of features If algorithm='randomized', must be less than or equal to the number of features The default value is useful for visualisation. For LSA, a value of 100 is recommended algorithm : {'arpack', 'randomized'}, default='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) | default: 2 |
n_iter | Number of iterations for randomized SVD solver. Not used by ARPACK. The default is larger than the default in :func:`~sklearn.utils.extmath.randomized_svd` to handle sparse matrices that may have large slowly decaying spectrum | default: 5 |
n_oversamples | Number of oversamples for randomized SVD solver. Not used by ARPACK See :func:`~sklearn.utils.extmath.randomized_svd` for a complete description .. versionadded:: 1.1 power_iteration_normalizer : {'auto', 'QR', 'LU', 'none'}, default='auto' Power iteration normalizer for randomized SVD solver Not used by ARPACK. See :func:`~sklearn.utils.extmath.randomized_svd` for more details .. versionadded:: 1.1 | default: 10 |
power_iteration_normalizer | default: "auto" | |
random_state | Used during randomized svd. Pass an int for reproducible results across
multiple function calls
See :term:`Glossary | default: null |
tol | Tolerance for ARPACK. 0 means machine precision. Ignored by randomized SVD solver. | default: 0.0 |