---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- will-clarke/autotrain-data-km3p-5cou-dikk
---

# Model Trained Using AutoTrain

- Problem type: Tabular regression

## Validation Metrics

- r2: -0.008598428559009497
- mse: 598.976166598342
- mae: 9.062458591043514
- rmse: 24.473989593001424
- rmsle: 1.2592486785782957
- loss: 24.473989593001424

## Best Params

- learning_rate: 0.05243299592316927
- reg_lambda: 6.717966298706072e-08
- reg_alpha: 1.6032915106085746e-08
- subsample: 0.5114836334096384
- colsample_bytree: 0.42603286105240046
- max_depth: 1
- early_stopping_rounds: 455
- n_estimators: 20000
- 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

```