Commit From AutoTrain

.gitattributes

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README.md

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+---
+tags:
+- autotrain
+- tabular
+- classification
+- tabular-classification
+datasets:
+- yuan1729/autotrain-data-a_
+co2_eq_emissions:
+  emissions: 3.4958451355282567
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Multi-class Classification
+- Model ID: 55839129832
+- CO2 Emissions (in grams): 3.4958
+
+## Validation Metrics
+
+- Loss: 0.667
+- Accuracy: 0.765
+- Macro F1: 0.608
+- Micro F1: 0.765
+- Weighted F1: 0.753
+- Macro Precision: 0.716
+- Micro Precision: 0.765
+- Weighted Precision: 0.760
+- Macro Recall: 0.576
+- Micro Recall: 0.765
+- Weighted Recall: 0.765
+
+## 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": ["col1", "col2", "col4", "col5", "col6", "col7", "col8", "col9", "col10", "col11", "col12", "col13", "col14", "col15", "col16", "col17", "col18", "col19", "col20", "col21", "col22", "col23", "col24", "col25", "col26", "col27", "col28", "col29", "col30", "col31", "col32", "col33", "col34", "col35", "col36", "col37", "col38", "col39", "col40", "col41", "col42"], "targets": ["target"], "model_type": "xgboost", "target_mapping": {"1": 0, "2": 1, "3": 2, "4": 3, "5": 4}}

model.joblib

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