{"flow":{"id":"17734","uploader":"12269","name":"sklearn.decomposition._factor_analysis.FactorAnalysis","custom_name":"sklearn.FactorAnalysis","class_name":"sklearn.decomposition._factor_analysis.FactorAnalysis","version":"1","external_version":"openml==0.10.2,sklearn==0.22.1","description":"Factor Analysis (FA)\n\nA simple linear generative model with Gaussian latent variables.\n\nThe observations are assumed to be caused by a linear transformation of\nlower dimensional latent factors and added Gaussian noise.\nWithout loss of generality the factors are distributed according to a\nGaussian with zero mean and unit covariance. The noise is also zero mean\nand has an arbitrary diagonal covariance matrix.\n\nIf we would restrict the model further, by assuming that the Gaussian\nnoise is even isotropic (all diagonal entries are the same) we would obtain\n:class:`PPCA`.\n\nFactorAnalysis performs a maximum likelihood estimate of the so-called\n`loading` matrix, the transformation of the latent variables to the\nobserved ones, using SVD based approach.","upload_date":"2020-05-18T23:44:12","language":"English","dependencies":"sklearn==0.22.1\nnumpy>=1.6.1\nscipy>=0.9","parameter":[{"name":"copy","data_type":"bool","default_value":"false","description":"Whether to make a copy of X. If ``False``, the input X gets overwritten\n during fitting"},{"name":"iterated_power","data_type":"int","default_value":"3","description":"Number of iterations for the power method. 3 by default. Only used\n if ``svd_method`` equals 'randomized'"},{"name":"max_iter","data_type":"int","default_value":"7723","description":"Maximum number of iterations"},{"name":"n_components","data_type":"int","default_value":"3","description":"Dimensionality of latent space, the number of components\n of ``X`` that are obtained after ``transform``\n If None, n_components is set to the number of features"},{"name":"noise_variance_init","data_type":"None","default_value":"null","description":"The initial guess of the noise variance for each feature\n If None, it defaults to np.ones(n_features)\n\nsvd_method : {'lapack', 'randomized'}\n Which SVD method to use. If 'lapack' use standard SVD from\n scipy.linalg, if 'randomized' use fast ``randomized_svd`` function\n Defaults to 'randomized'. For most applications 'randomized' will\n be sufficiently precise while providing significant speed gains\n Accuracy can also be improved by setting higher values for\n `iterated_power`. If this is not sufficient, for maximum precision\n you should choose 'lapack'"},{"name":"random_state","data_type":"int","default_value":"42","description":"If int, random_state is the seed used by the random number generator;\n If RandomState instance, random_state is the random number generator;\n If None, the random number generator is the RandomState instance used\n by `np.random`. Only used when ``svd_method`` equals 'randomized'."},{"name":"svd_method","data_type":[],"default_value":"\"lapack\"","description":[]},{"name":"tol","data_type":"float","default_value":"1.841563435236402","description":"Stopping tolerance for log-likelihood increase"}],"tag":["openml-python","python","scikit-learn","sklearn","sklearn_0.22.1"]}}