---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- autotrain-uljkp-sdhgs/autotrain-data
---

# Model Trained Using AutoTrain

- Problem type: Tabular regression

## Validation Metrics

- r2: 0.9900762497798218
- mse: 10317.805777253338
- mae: 74.54517527770996
- rmse: 101.57660053995377
- rmsle: 0.042811727450114016
- loss: 101.57660053995377

## Best Params

- learning_rate: 0.016479102091350954
- reg_lambda: 0.3449233788687026
- reg_alpha: 3.244557908377455e-07
- subsample: 0.5379679408548034
- colsample_bytree: 0.9050706969365716
- max_depth: 4
- early_stopping_rounds: 293
- 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

```