
# Model Trained Using AutoTrain

- Problem type: Tabular classification

## Validation Metrics

- auc: 0.5845690136815017
- logloss: 0.3365109999879715
- f1: 0.9423455684870188
- accuracy: 0.8926666666666667
- precision: 0.8949158306410474
- recall: 0.9950841368878806
- loss: 0.3365109999879715

## Best Params

- learning_rate: 0.024903253168743332
- reg_lambda: 7.491240117115577e-05
- reg_alpha: 4.545201602072796e-08
- subsample: 0.5376101864226687
- colsample_bytree: 0.7150042050371038
- max_depth: 2
- early_stopping_rounds: 368
- n_estimators: 7000
- eval_metric: logloss

## 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

```

{"tags": ["autotrain", "tabular", "classification", "tabular-classification"], "datasets": ["autotrain-e7z27-ikr13/autotrain-data"]}
joblib autotrain tabular classification tabular-classification dataset:autotrain-e7z27-ikr13/autotrain-data region:us