
# Model Trained Using AutoTrain

- Problem type: Tabular classification

## Validation Metrics

- auc: 1.0
- logloss: 0.02560930807260143
- f1: 1.0
- accuracy: 1.0
- precision: 1.0
- recall: 1.0
- loss: 0.02560930807260143

## Best Params

- learning_rate: 0.22611071522667908
- reg_lambda: 0.03493387873764126
- reg_alpha: 2.9837721474835582e-05
- subsample: 0.912493466832241
- colsample_bytree: 0.50276453147468
- max_depth: 1
- early_stopping_rounds: 144
- 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-700h1-dxj41/autotrain-data"]}
joblib autotrain tabular classification tabular-classification dataset:autotrain-700h1-dxj41/autotrain-data region:us