PearsonSelector¶
- class mastml.feature_selectors.PearsonSelector(threshold_between_features, threshold_with_target, flag_highly_correlated_features, n_features_to_select)[source]¶
Bases:
BaseSelector
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
n_features_to_select: (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, y)transform
(X)Methods Documentation