Feat Selectors#
Different base obj choices for the FeatSelector
are shown below
The exact str indicator, as passed to the obj param is represented by the sub-heading (within “”)
The available feat selectors are further broken down by which can work with different problem_types.
Additionally, a link to the original models documentation as well as the implemented parameter distributions are shown.
binary#
“rfe”#
Base Class Documentation:
sklearn.feature_selection.RFE
Param Distributions
“base rfe”
n_features_to_select: None“rfe num feats dist”
n_features_to_select: Scalar(init=0.5, lower=0.1, upper=0.99).set_mutation(sigma=0.14833333333333334).set_bounds(full_range_sampling=False, lower=0.1, upper=0.99)
“selector”#
Base Class Documentation:
BPt.extensions.feat_selectors.FeatureSelector
Param Distributions
“random”
mask: 'sets as random features'“searchable”
mask: 'sets as hyperparameters'
“univariate selection c”#
Base Class Documentation:
sklearn.feature_selection.SelectPercentile
Param Distributions
“base univar fs classifier”
score_func: <function f_classif at 0x7f66935c0dc0> percentile: 50“univar fs classifier dist”
score_func: <function f_classif at 0x7f66935c0dc0> percentile: Scalar(init=50, lower=1, upper=99).set_mutation(sigma=16.333333333333332).set_bounds(full_range_sampling=False, lower=1, upper=99)“univar fs c keep more”
score_func: <function f_classif at 0x7f66935c0dc0> percentile: Scalar(init=75, lower=50, upper=99).set_mutation(sigma=8.166666666666666).set_bounds(full_range_sampling=False, lower=50, upper=99)“univar fs c keep less”
score_func: <function f_classif at 0x7f66935c0dc0> percentile: Scalar(init=25, lower=1, upper=50).set_mutation(sigma=8.166666666666666).set_bounds(full_range_sampling=False, lower=1, upper=50)
“variance threshold”#
Base Class Documentation:
sklearn.feature_selection.VarianceThreshold
Param Distributions
“default”
defaults only
regression#
“rfe”#
Base Class Documentation:
sklearn.feature_selection.RFE
Param Distributions
“base rfe”
n_features_to_select: None“rfe num feats dist”
n_features_to_select: Scalar(init=0.5, lower=0.1, upper=0.99).set_mutation(sigma=0.14833333333333334).set_bounds(full_range_sampling=False, lower=0.1, upper=0.99)
“selector”#
Base Class Documentation:
BPt.extensions.feat_selectors.FeatureSelector
Param Distributions
“random”
mask: 'sets as random features'“searchable”
mask: 'sets as hyperparameters'
“univariate selection r”#
Base Class Documentation:
sklearn.feature_selection.SelectPercentile
Param Distributions
“base univar fs regression”
score_func: <function f_regression at 0x7f66935c2040> percentile: 50“univar fs regression dist”
score_func: <function f_regression at 0x7f66935c2040> percentile: Scalar(init=50, lower=1, upper=99).set_mutation(sigma=16.333333333333332).set_bounds(full_range_sampling=False, lower=1, upper=99)“univar fs r keep more”
score_func: <function f_regression at 0x7f66935c2040> percentile: Scalar(init=75, lower=50, upper=99).set_mutation(sigma=8.166666666666666).set_bounds(full_range_sampling=False, lower=50, upper=99)“univar fs r keep less”
score_func: <function f_regression at 0x7f66935c2040> percentile: Scalar(init=25, lower=1, upper=50).set_mutation(sigma=8.166666666666666).set_bounds(full_range_sampling=False, lower=1, upper=50)
“variance threshold”#
Base Class Documentation:
sklearn.feature_selection.VarianceThreshold
Param Distributions
“default”
defaults only
categorical#
“rfe”#
Base Class Documentation:
sklearn.feature_selection.RFE
Param Distributions
“base rfe”
n_features_to_select: None“rfe num feats dist”
n_features_to_select: Scalar(init=0.5, lower=0.1, upper=0.99).set_mutation(sigma=0.14833333333333334).set_bounds(full_range_sampling=False, lower=0.1, upper=0.99)
“selector”#
Base Class Documentation:
BPt.extensions.feat_selectors.FeatureSelector
Param Distributions
“random”
mask: 'sets as random features'“searchable”
mask: 'sets as hyperparameters'
“univariate selection c”#
Base Class Documentation:
sklearn.feature_selection.SelectPercentile
Param Distributions
“base univar fs classifier”
score_func: <function f_classif at 0x7f66935c0dc0> percentile: 50“univar fs classifier dist”
score_func: <function f_classif at 0x7f66935c0dc0> percentile: Scalar(init=50, lower=1, upper=99).set_mutation(sigma=16.333333333333332).set_bounds(full_range_sampling=False, lower=1, upper=99)“univar fs c keep more”
score_func: <function f_classif at 0x7f66935c0dc0> percentile: Scalar(init=75, lower=50, upper=99).set_mutation(sigma=8.166666666666666).set_bounds(full_range_sampling=False, lower=50, upper=99)“univar fs c keep less”
score_func: <function f_classif at 0x7f66935c0dc0> percentile: Scalar(init=25, lower=1, upper=50).set_mutation(sigma=8.166666666666666).set_bounds(full_range_sampling=False, lower=1, upper=50)
“variance threshold”#
Base Class Documentation:
sklearn.feature_selection.VarianceThreshold
Param Distributions
“default”
defaults only