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wine-quality
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ModelKit Tag Metadata
- Digest
- Author
- N/A
- Date added
- Size
- 24.9MB
- Total pulls
- 6
Package
- Name
- white-wine-quality-predictor
- Version
- 2.1
- Authors
- N/A
- Description
- An ML model to predict the quality of white wine.
Model
- Name
- white-wine-xgb
- Path
- model/model.pkl
- License
- Apache 2.0
- Parts
- N/A
- Parameters
{
"MLFlow": {
"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
- Wine Quality Prediction.ipynb
- Jupyter notebook with the model code
- mlflow-run.py
- MLFlow experiment run with a ModelKit integration
- requirements.txt
- Python dependencies
Docs
- docs/README.md
- Model card
- docs/LICENSE
- Apache 2.0 license
- docs/CDLA-Permissive-2.0.md
- CDLA license for datasets
- mlflow-artifacts/confusion_matrix.png
- MLFlow confusion matrix plot
- mlflow-artifacts/roc_curve_plot.png
- MLFlow ROC curve
- mlflow-artifacts/precision_recall_curve_plot.png
- MLFlow precision recall curve
- mlflow-artifacts/per_class_metrics.csv
- MLFlow per-class metrics
- mlflow-artifacts/shap_beeswarm_plot.png
- MLFlow SHAP beeswarm plot
- mlflow-artifacts/shap_feature_importance_plot.png
- MLFlow SHAP feature importance
- mlflow-artifacts/shap_summary_plot.png
- MLFlow SHAP summary plot