Commit From AutoTrain

.gitattributes

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README.md

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+---
+tags:
+- autotrain
+- tabular
+- classification
+- tabular-classification
+datasets:
+- reesu/autotrain-data-wine_quality
+co2_eq_emissions:
+  emissions: 8.276808778335907
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 3195889861
+- CO2 Emissions (in grams): 8.2768
+
+## Validation Metrics
+
+- Loss: 0.995
+- Accuracy: 0.569
+- Macro F1: 0.296
+- Micro F1: 0.569
+- Weighted F1: 0.543
+- Macro Precision: 0.447
+- Micro Precision: 0.569
+- Weighted Precision: 0.558
+- Macro Recall: 0.283
+- Micro Recall: 0.569
+- Weighted Recall: 0.569
+
+## Usage
+
+```python
+import json
+import joblib
+import pandas as pd
+
+model = joblib.load('model.joblib')
+config = json.load(open('config.json'))
+
+features = config['features']
+
+# data = pd.read_csv("data.csv")
+data = data[features]
+data.columns = ["feat_" + str(col) for col in data.columns]
+
+predictions = model.predict(data)  # or model.predict_proba(data)
+
+```

config.json

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+{"features": ["Id", "fixed acidity", "volatile acidity", "citric acid", "residual sugar", "chlorides", "free sulfur dioxide", "total sulfur dioxide", "density", "pH", "sulphates", "alcohol"], "targets": ["target"], "model_type": "xgboost", "target_mapping": {"3": 0, "4": 1, "5": 2, "6": 3, "7": 4, "8": 5}}

model.joblib

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