---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- giulioappetito/churn_dataset_giulioappetito
---

# Model Trained Using AutoTrain

- Problem type: Tabular regression

## Validation Metrics

- r2: 0.75551694767912
- mse: 0.06038101818653434
- mae: 0.12572727079081708
- rmse: 0.24572549356250023
- rmsle: 0.1700195996741877
- loss: 0.24572549356250023

## Best Params

- learning_rate: 0.19966547950225813
- reg_lambda: 1.1910980465515898e-05
- reg_alpha: 0.003345407176272181
- subsample: 0.5134686751829827
- colsample_bytree: 0.7469701482100698
- max_depth: 7
- early_stopping_rounds: 407
- n_estimators: 15000
- eval_metric: rmse

## 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

```