get_features_by_backward_and_tffs

Description

The get_features_by_backward_and_tffs function performs feature selection based on Backward Selection combined with Feature Selection based on Top Frequency (TFFS). This function helps identify important features in a dataset by leveraging the TFFS algorithm from the pypi-tffs repository and Backward Selection.

Parameters

Workflow

1. Feature Selection based on Top Frequency (TFFS):

   • Apply the TFFS algorithm from pypi-tffs to identify features that frequently appear as important across multiple Random Forest model runs.
   • Retain the top percent_tffs% most frequently selected features.

2. Backward Selection:

   • Iteratively remove the least important features and evaluate model performance to identify those with the least impact.

3. Backward Selection:

   • From the set of features selected in the TFFS step, retain percent_backward% of the most impactful features.

Feature Selection Methods

In addition to Backward Selection, this package includes multiple classical feature selection methods combined with TFFS:

• Forward Selection (get_features_by_forward_and_tffs)
• Recursive Feature Elimination (RFE) (get_features_by_recursive_and_tffs)
• Pearson Correlation (P.C.) (get_features_by_pc_and_tffs)
• Mutual Information (M.I.) (get_features_by_mi_and_tffs)
• Fish Score (get_features_by_fs_and_tffs)
• Lasso Regression (get_features_by_lasso_and_tffs)

Example Usage

import pandas as pd
from hybridtffs import get_features_by_backward_and_tffs

# Create a sample DataFrame
data = pd.DataFrame({
    'class': [0, 1, 0, 1, 2, 0, 1, 2, 0, 1],
    'feature_1': [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
    'feature_2': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'feature_3': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
    'feature_4': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'feature_5': [5, 6, 7, 8, 9, 10, 11, 12, 13, 14],
    'feature_6': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
    'feature_7': [2, 3, 4, 5, 6, 7, 8, 9, 10, 11],
    'feature_8': [3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
    'feature_9': [4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
})

# Run the function
selected_features = get_features_by_backward_and_tffs(
    data,
    percent_tffs=50,
    number_run=10,
    n_estimators=100,
    percent_backward=30
)

print("Selected features:", selected_features)

Author

Vu Thi Kieu Anh

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