---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- nicoler229/autotrain-data-renp-vcyx-5hff
---

# Model Trained Using AutoTrain

- Problem type: Tabular regression

## Validation Metrics

- r2: 0.8987710422047952
- mse: 15.386801584871137
- mae: 3.1008129119873047
- rmse: 3.9226013798079378
- rmsle: 0.049014949862444
- loss: 3.9226013798079378

## Best Params

- learning_rate: 0.09858308825036341
- reg_lambda: 1.7244892825164977e-06
- reg_alpha: 0.004880162297132929
- subsample: 0.5918267532876357
- colsample_bytree: 0.6228647593929555
- max_depth: 8
- early_stopping_rounds: 440
- n_estimators: 7000
- 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

```