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

  1. “base rfe”

    n_features_to_select: None
    
  2. “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

  1. “random”

    mask: 'sets as random features'
    
  2. “searchable”

    mask: 'sets as hyperparameters'
    

“univariate selection c”#

Base Class Documentation: sklearn.feature_selection.SelectPercentile

Param Distributions

  1. “base univar fs classifier”

    score_func: <function f_classif at 0x7f66935c0dc0>
    percentile: 50
    
  2. “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)
    
  3. “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)
    
  4. “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

  1. “default”

    defaults only
    

regression#

“rfe”#

Base Class Documentation: sklearn.feature_selection.RFE

Param Distributions

  1. “base rfe”

    n_features_to_select: None
    
  2. “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

  1. “random”

    mask: 'sets as random features'
    
  2. “searchable”

    mask: 'sets as hyperparameters'
    

“univariate selection r”#

Base Class Documentation: sklearn.feature_selection.SelectPercentile

Param Distributions

  1. “base univar fs regression”

    score_func: <function f_regression at 0x7f66935c2040>
    percentile: 50
    
  2. “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)
    
  3. “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)
    
  4. “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

  1. “default”

    defaults only
    

categorical#

“rfe”#

Base Class Documentation: sklearn.feature_selection.RFE

Param Distributions

  1. “base rfe”

    n_features_to_select: None
    
  2. “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

  1. “random”

    mask: 'sets as random features'
    
  2. “searchable”

    mask: 'sets as hyperparameters'
    

“univariate selection c”#

Base Class Documentation: sklearn.feature_selection.SelectPercentile

Param Distributions

  1. “base univar fs classifier”

    score_func: <function f_classif at 0x7f66935c0dc0>
    percentile: 50
    
  2. “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)
    
  3. “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)
    
  4. “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

  1. “default”

    defaults only