Classification Helper
Train and evaluate classification models with automatic model selection.
Features Auto Model Selection: Compare multiple classifiers Hyperparameter Tuning: Grid/random search Evaluation Metrics: Accuracy, precision, recall, F1, ROC-AUC Cross-Validation: K-fold validation Confusion Matrix: Detailed error analysis Feature Importance: Top predictive features Model Export: Save trained models CLI Usage python classification_helper.py --data train.csv --target class --test test.csv --output model.pkl
Dependencies scikit-learn>=1.3.0 pandas>=2.0.0 numpy>=1.24.0 matplotlib>=3.7.0 seaborn>=0.12.0