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sklearn.decomposition._factor_analysis.FactorAnalysis
sklearn.FactorAnalysis
sklearn.decomposition._factor_analysis.FactorAnalysis
1
openml==0.10.2,sklearn==0.22.1
Factor Analysis (FA)
A simple linear generative model with Gaussian latent variables.
The observations are assumed to be caused by a linear transformation of
lower dimensional latent factors and added Gaussian noise.
Without loss of generality the factors are distributed according to a
Gaussian with zero mean and unit covariance. The noise is also zero mean
and has an arbitrary diagonal covariance matrix.
If we would restrict the model further, by assuming that the Gaussian
noise is even isotropic (all diagonal entries are the same) we would obtain
:class:`PPCA`.
FactorAnalysis performs a maximum likelihood estimate of the so-called
`loading` matrix, the transformation of the latent variables to the
observed ones, using SVD based approach.
2020-05-18T23:44:12
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
copy
bool
false
Whether to make a copy of X. If ``False``, the input X gets overwritten
during fitting
iterated_power
int
3
Number of iterations for the power method. 3 by default. Only used
if ``svd_method`` equals 'randomized'
max_iter
int
7723
Maximum number of iterations
n_components
int
3
Dimensionality of latent space, the number of components
of ``X`` that are obtained after ``transform``
If None, n_components is set to the number of features
noise_variance_init
None
null
The initial guess of the noise variance for each feature
If None, it defaults to np.ones(n_features)
svd_method : {'lapack', 'randomized'}
Which SVD method to use. If 'lapack' use standard SVD from
scipy.linalg, if 'randomized' use fast ``randomized_svd`` function
Defaults to 'randomized'. For most applications 'randomized' will
be sufficiently precise while providing significant speed gains
Accuracy can also be improved by setting higher values for
`iterated_power`. If this is not sufficient, for maximum precision
you should choose 'lapack'
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`. Only used when ``svd_method`` equals 'randomized'.
svd_method
"lapack"
tol
float
1.841563435236402
Stopping tolerance for log-likelihood increase
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1