---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- Notaspy1234/autotrain-data-Autotrain3
---

# Model Trained Using AutoTrain

- Problem type: Tabular regression

## Validation Metrics

- r2: 0.9753017864826334
- mse: 0.3290419495851166
- mae: 0.47130432128906286
- rmse: 0.5736217826975512
- rmsle: 0.057378419858521094
- loss: 0.5736217826975512

## Best Params

- learning_rate: 0.022993157585548683
- reg_lambda: 0.0030417803769039035
- reg_alpha: 0.17755049688249555
- subsample: 0.33171622212758833
- colsample_bytree: 0.10545502763287017
- max_depth: 8
- early_stopping_rounds: 387
- 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

```