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Table 1 Logistic regression feature coefficients and random forest feature importances for the breast cancer data

From: Comparison of feature importance measures as explanations for classification models

Feature Coefficient Importance Feature Coefficient Importance
Radius 1 − 0.088 0.034 Texture 1 0.103 0.016
Perimeter 1 − 0.061 0.041 Area 1 − 0.020 0.041
Smoothness 1 0.061 0.006 Compactness 1 − 0.702 0.011
Concavity 1 0.831 0.042 Concave points 1 1.114 0.105
Symmetry 1 − 0.062 0.004 Fractal dimension 1 0.031 0.004
Radius 2 2.207 0.014 Texture 2 − 0.396 0.004
Perimeter 2 0.042 0.013 Area 2 0.729 0.032
Smoothness 2 0.211 0.004 Compactness 2 − 0.464v 0.005
Concavity 2 − 0.133 0.007 Concave points 2 0.322 0.005
Symmetry 2 − 0.204 0.004 Fractal dimension 2 − 0.827 0.005
Radius 3 3.170 0.113 Texture 3 1.723 0.019
Perimeter 3 0.482 0.145 Area 3 0.747 0.120
Smoothness 3 0.573 0.013 Compactness 3 − 0.089 0.015
Concavity 3 0.766 0.032 Concave points 3 1.185 0.130
Symmetry 3 0.692 0.010 Fractal dimension 3 0.423 0.006
  1. Bolded are the nine features detected with random forest and nine most important features with regression, ranked based on the p-value