---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- autotrain-eno5u-hy49n/autotrain-data
---

# Model Trained Using AutoTrain

- Problem type: Tabular regression

## Validation Metrics

- r2: 0.9988073651686958
- mse: 337854.8880689932
- mae: 398.9371570016889
- rmse: 581.2528606974706
- rmsle: 0.01184006241771929
- loss: 581.2528606974706

## Best Params

- learning_rate: 0.09168110099890295
- reg_lambda: 0.11592417839619104
- reg_alpha: 0.0010410090431649107
- subsample: 0.357364833100802
- colsample_bytree: 0.9155936367985213
- max_depth: 7
- early_stopping_rounds: 158
- 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

```