---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- autotrain-fbyte-l9vfz/autotrain-data
---

# Model Trained Using AutoTrain

- Problem type: Tabular regression

## Validation Metrics

- r2: 0.33913886288678097
- mse: 0.13878083879377598
- mae: 0.2991213083267212
- rmse: 0.37253300363025016
- rmsle: 0.15062628429771513
- loss: 0.37253300363025016

## Best Params

- learning_rate: 0.017092100292696658
- reg_lambda: 2.08790995148619e-05
- reg_alpha: 5.763917500537152e-06
- subsample: 0.38707603768089427
- colsample_bytree: 0.547982260603956
- max_depth: 1
- early_stopping_rounds: 102
- 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

```