Using intrinsic features
precision
recall
f1-score
support
0.0
0.66
0.76 0.60
0.71
1307 1258 2565 2565 2565
1.0
0.71
0.65 0.68 0.68 0.68
accuracy macro avg
0.69 0.69
0.68 0.68
weighted avg
Using graph features
precision
recall
f1-score
support
0.0
0.72 0.71
0.72 0.70
0.72 0.71 0.71 0.71 0.71
1307 1258 2565 2565 2565
1.0
accuracy macro avg
0.71 0.71
0.71 0.71
weighted avg
Top 20 Feature Importances
Top 20 Feature Importances
0.30 0.25 0.20 0.15 0.10 0.05 0.00
0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00
The key features mimic those of the model that lacks graph features; however, it's been enriched with graph metrics. Additionally, the relevance of each feature sees a significant boost compared to their counterparts in the base model. This suggests that including fewer graph features can augment the model's accuracy and insight, offering a greater yield than a wide range of features used in a model without graph elements
© 2023 Fractal Analytics Inc. All rights reserved
06
Made with FlippingBook - PDF hosting