Model Performance Dashboard

Interactive comparison of 7 machine-learning models

Best Model
Best Accuracy
Models Compared
7
Model Accuracy Comparison
Precision, Recall & F1 Score
ROC-AUC Comparison
Confusion Matrices
Confusion Matrices
Feature Importance
Feature Importance
Complete Metrics Table
Model Accuracy Precision Recall F1 Score ROC-AUC
Logistic Regression 0.7359 0.6562 0.5185 0.5793 0.8394
Decision Tree 0.7013 0.5714 0.5926 0.5818 0.6763
Random Forest 0.7359 0.6562 0.5185 0.5793 0.8236
XGBoost 0.7446 0.6528 0.5803 0.6144 0.7944
LightGBM 0.7316 0.6267 0.5803 0.6026 0.8111
CatBoost 0.7576 0.7049 0.5309 0.6056 0.8328
Hybrid RF-GBDT 0.7576 0.6812 0.5803 0.6267 0.8342