{"flow":{"id":"18874","uploader":"6691","name":"sklearn.decomposition.pca.PCA","custom_name":"sklearn.PCA","class_name":"sklearn.decomposition.pca.PCA","version":"11","external_version":"openml==0.12.2,sklearn==0.18.1","description":"Principal component analysis (PCA)\n\nLinear dimensionality reduction using Singular Value Decomposition of the\ndata to project it to a lower dimensional space.\n\nIt uses the LAPACK implementation of the full SVD or a randomized truncated\nSVD by the method of Halko et al. 2009, depending on the shape of the input\ndata and the number of components to extract.\n\nIt can also use the scipy.sparse.linalg ARPACK implementation of the\ntruncated SVD.\n\nNotice that this class does not support sparse input. See\n:class:`TruncatedSVD` for an alternative with sparse data.","upload_date":"2021-08-13T19:19:24","language":"English","dependencies":"sklearn==0.18.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"copy","data_type":"bool","default_value":"true","description":"If False, data passed to fit are overwritten and running\n fit(X).transform(X) will not yield the expected results,\n use fit_transform(X) instead"},{"name":"iterated_power","data_type":"int","default_value":"\"auto\"","description":"Number of iterations for the power method computed by\n svd_solver == 'randomized'\n\n .. versionadded:: 0.18.0"},{"name":"n_components","data_type":"int","default_value":"null","description":"Number of components to keep\n if n_components is not set all components are kept::\n\n n_components == min(n_samples, n_features)\n\n if n_components == 'mle' and svd_solver == 'full', Minka's MLE is used\n to guess the dimension\n if ``0 < n_components < 1`` and svd_solver == 'full', select the number\n of components such that the amount of variance that needs to be\n explained is greater than the percentage specified by n_components\n n_components cannot be equal to n_features for svd_solver == 'arpack'"},{"name":"random_state","data_type":"int or RandomState instance or None","default_value":"null","description":"Pseudo Random Number generator seed control. If None, use the\n numpy.random singleton. Used by svd_solver == 'arpack' or 'randomized'\n\n .. versionadded:: 0.18.0"},{"name":"svd_solver","data_type":"string","default_value":"\"auto\"","description":"auto :\n the solver is selected by a default policy based on `X.shape` and\n `n_components`: if the input data is larger than 500x500 and the\n number of components to extract is lower than 80% of the smallest\n dimension of the data, then the more efficient 'randomized'\n method is enabled. Otherwise the exact full SVD is computed and\n optionally truncated afterwards\n full :\n run exact full SVD calling the standard LAPACK solver via\n `scipy.linalg.svd` and select the components by postprocessing\n arpack :\n run SVD truncated to n_components calling ARPACK solver via\n `scipy.sparse.linalg.svds`. It requires strictly\n 0 < n_components < X.shape[1]\n randomized :\n run randomized SVD by the method of Halko et al\n\n .. versionadded:: 0.18.0"},{"name":"tol","data_type":"float","default_value":"0.0","description":"Tolerance for singular values computed by svd_solver == 'arpack'\n\n .. versionadded:: 0.18.0"},{"name":"whiten","data_type":"bool","default_value":"false","description":"When True (False by default) the `components_` vectors are multiplied\n by the square root of n_samples and then divided by the singular values\n to ensure uncorrelated outputs with unit component-wise variances\n\n Whitening will remove some information from the transformed signal\n (the relative variance scales of the components) but can sometime\n improve the predictive accuracy of the downstream estimators by\n making their data respect some hard-wired assumptions"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.18.1"]}}