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README.md

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+
+---
+tags:
+- autotrain
+- tabular
+- classification
+- tabular-classification
+datasets:
+- autotrain-vdbyj-hps42/autotrain-data
+---
+
+# Model Trained Using AutoTrain
+
+- Problem type: Tabular classification
+
+## Validation Metrics
+
+- logloss: 0.28027260106321344
+- accuracy: 0.8666666666666667
+- mlogloss: 0.28027260106321344
+- f1_macro: 0.8611111111111112
+- f1_micro: 0.8666666666666667
+- f1_weighted: 0.8611111111111112
+- precision_macro: 0.9047619047619048
+- precision_micro: 0.8666666666666667
+- precision_weighted: 0.9047619047619048
+- recall_macro: 0.8666666666666667
+- recall_micro: 0.8666666666666667
+- recall_weighted: 0.8666666666666667
+- loss: 0.28027260106321344
+
+## Best Params
+
+- C: 360.26071213844074
+- fit_intercept: False
+- solver: saga
+- penalty: l2
+
+## 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]
+
+predictions = model.predict(data)  # or model.predict_proba(data)
+
+# predictions can be converted to original labels using label_encoders.pkl
+
+```

model.joblib

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target_encoders.joblib

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training_params.json

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+{"data_path": "autotrain-vdbyj-hps42/autotrain-data", "model": "logistic_regression", "username": "AlexVplle", "seed": 42, "train_split": "train", "valid_split": "validation", "project_name": "autotrain-vdbyj-hps42", "push_to_hub": true, "id_column": "autotrain_id", "target_columns": ["autotrain_label"], "categorical_columns": null, "numerical_columns": null, "task": "classification", "num_trials": 10, "time_limit": 600, "categorical_imputer": "most_frequent", "numerical_imputer": "median", "numeric_scaler": "robust"}