Transformers#
Different base obj choices for the Transformer are shown below
The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”)
Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown.
“pca”#
Base Class Documentation:
sklearn.decomposition.PCAParam Distributions
“default”
defaults only“pca var search”
n_components: Scalar(init=0.75, lower=0.1, upper=0.99).set_mutation(sigma=0.14833333333333334).set_bounds(full_range_sampling=False, lower=0.1, upper=0.99) svd_solver: 'full'
“sparse pca”#
Base Class Documentation:
sklearn.decomposition.SparsePCAParam Distributions
“default”
defaults only
“mini batch sparse pca”#
Base Class Documentation:
sklearn.decomposition.MiniBatchSparsePCAParam Distributions
“default”
defaults only
“factor analysis”#
Base Class Documentation:
sklearn.decomposition.FactorAnalysisParam Distributions
“default”
defaults only
“dictionary learning”#
Base Class Documentation:
sklearn.decomposition.DictionaryLearningParam Distributions
“default”
defaults only
“mini batch dictionary learning”#
Base Class Documentation:
sklearn.decomposition.MiniBatchDictionaryLearningParam Distributions
“default”
defaults only
“fast ica”#
“incremental pca”#
Base Class Documentation:
sklearn.decomposition.IncrementalPCAParam Distributions
“default”
defaults only
“kernel pca”#
Base Class Documentation:
sklearn.decomposition.KernelPCAParam Distributions
“default”
defaults only
“nmf”#
“truncated svd”#
Base Class Documentation:
sklearn.decomposition.TruncatedSVDParam Distributions
“default”
defaults only
“one hot encoder”#
Base Class Documentation:
sklearn.preprocessing.OneHotEncoderParam Distributions
“ohe”
sparse: False handle_unknown: 'ignore'
“dummy coder”#
Base Class Documentation:
sklearn.preprocessing.OneHotEncoderParam Distributions
“dummy code”
sparse: False drop: 'first' handle_unknown: 'error'