
# Model Trained Using AutoTrain

- Problem type: Tabular classification

## Validation Metrics

- auc: 1.0
- logloss: 0.025053335605932175
- f1: 1.0
- accuracy: 1.0
- precision: 1.0
- recall: 1.0
- loss: 0.025053335605932175

## Best Params

- learning_rate: 0.12345397966448246
- reg_lambda: 2.4294163996630167e-08
- reg_alpha: 3.168245100984154e-06
- subsample: 0.8396911707024661
- colsample_bytree: 0.8419140279836324
- max_depth: 8
- early_stopping_rounds: 414
- n_estimators: 20000
- 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-iben7-xhpuo/autotrain-data"]}
joblib autotrain tabular classification tabular-classification dataset:autotrain-iben7-xhpuo/autotrain-data region:us