PearsonSelector

class mastml.legos.feature_selectors.PearsonSelector(threshold_between_features, threshold_with_target, remove_highly_correlated_features, k_features)

Bases: object

Class custom-written for MAST-ML to conduct selection of features based on Pearson correlation coefficent between features and target. Can also be used for dimensionality reduction by removing redundant features highly correlated with each other.

Args:

threshold_between_features: (float), the threshold to decide whether redundant features are removed. Should be a decimal value between 0 and 1. Only used if remove_highly_correlated_features is True

threshold_with_target: (float), the threshold to decide whether a given feature is sufficiently correlated with the target feature and thus kept as a selected feature. Should be a decimal value between 0 and 1.

remove_highly_correlated_features: (bool), whether to remove features highly correlated with each other

k_features: (int), the number of features to select

Methods:

fit: performs feature selection

Args:

X: (dataframe), dataframe of X features

y: (dataframe), dataframe of y data

Returns:

None

transform: performs the transform to generate output of only selected features

Args:

X: (dataframe), dataframe of X features

Returns:

dataframe: (dataframe), dataframe of selected X features

Methods Summary

fit(X, savepath[, y, Xgroups])
transform(X)

Methods Documentation

fit(X, savepath, y=None, Xgroups=None)

transform(X)