| Issue | #Downvotes for this reason | By |
|---|
| copy | If False, data passed to fit are overwritten and running fit(X).transform(X) will not yield the expected results, use fit_transform(X) instead | default: true |
| iterated_power | Number of iterations for the power method computed by svd_solver == 'randomized' Must be of range [0, infinity) .. versionadded:: 0.18.0 | default: "auto" |
| n_components | Number of components to keep if n_components is not set all components are kept:: n_components == min(n_samples, n_features) If ``n_components == 'mle'`` and ``svd_solver == 'full'``, Minka's MLE is used to guess the dimension. Use of ``n_components == 'mle'`` will interpret ``svd_solver == 'auto'`` as ``svd_solver == 'full'`` If ``0 < n_components < 1`` and ``svd_solver == 'full'``, select the number of components such that the amount of variance that needs to be explained is greater than the percentage specified by n_components If ``svd_solver == 'arpack'``, the number of components must be strictly less than the minimum of n_features and n_samples Hence, the None case results in:: n_components == min(n_samples, n_features) - 1 | default: 3 |
| n_oversamples | This parameter is only relevant when `svd_solver="randomized"` It corresponds to the additional number of random vectors to sample the range of `X` so as to ensure proper conditioning. See :func:`~sklearn.utils.extmath.randomized_svd` for more details .. 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 when the 'arpack' or 'randomized' solvers are used. Pass an int
for reproducible results across multiple function calls
See :term:`Glossary | default: null |
| svd_solver | default: "auto" | |
| tol | Tolerance for singular values computed by svd_solver == 'arpack' Must be of range [0.0, infinity) .. versionadded:: 0.18.0 | default: 0.0 |
| whiten | When True (False by default) the `components_` vectors are multiplied by the square root of n_samples and then divided by the singular values to ensure uncorrelated outputs with unit component-wise variances Whitening will remove some information from the transformed signal (the relative variance scales of the components) but can sometime improve the predictive accuracy of the downstream estimators by making their data respect some hard-wired assumptions svd_solver : {'auto', 'full', 'arpack', 'randomized'}, default='auto' If auto : The solver is selected by a default policy based on `X.shape` and `n_components`: if the input data is larger than 500x500 and the number of components to extract is lower than 80% of the smallest dimension of the data, then the more efficient 'randomized' method is enabled. Otherwise the exact full SVD is computed and optionally truncated afterwards If full : run exact full SVD calling th... | default: false |