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wine-quality
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ModelKit Tag Metadata
- Digest
- Author
- N/A
- Date added
- Size
- 2.9MB
- Total pulls
- 2
Package
- Name
- white-wine-quality-predictor
- Version
- 1.0
- Authors
- N/A
- Description
- Predict the quality of white wine
Model
- Name
- white-wine-xgb
- Path
- model/model.pkl
- License
- Apache 2.0
- Parts
- N/A
- Parameters
{
"run_info": {
"run_id": "611dd98a4fbc42f78c20c559337f8202",
"run_name": "industrious-loon-433",
"artifact_uri": "mlflow-artifacts:/693112939931118568/611dd98a4fbc42f78c20c559337f8202/artifacts",
"experiment_id": "693112939931118568"
},
"result_info": {
"metrics": {
"score": 0.6499690785405071,
"roc_auc": 0.8302286512800172,
"f1_score": 0.6408572999870976,
"log_loss": 0.9028615945319611,
"recall_score": 0.6499690785405071,
"example_count": 1617,
"accuracy_score": 0.6499690785405071,
"precision_score": 0.6526749219675065
}
},
"best_hyperparameters": {
"max_depth": 10,
"n_estimators": 50,
"learning_rate": 0.1
}
}
Datasets
- white-wine-quality.csv
- data/white-wine-quality.csv
Codebases
- demo.py
- ModelKit integration with MLflow
- requirements.txt
- Python dependencies
Docs
- README.md
- Model card
- LICENSE
- License file
- artifacts/confusion_matrix.png
- Image from experiment run industrious-loon-433
- artifacts/roc_curve_plot.png
- Image from experiment run industrious-loon-433
- artifacts/precision_recall_curve_plot.png
- Image from experiment run industrious-loon-433
- artifacts/per_class_metrics.csv
- Image from experiment run industrious-loon-433
- artifacts/shap_beeswarm_plot.png
- Image from experiment run industrious-loon-433
- artifacts/shap_feature_importance_plot.png
- Image from experiment run industrious-loon-433
- artifacts/shap_summary_plot.png
- Image from experiment run industrious-loon-433