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---
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
```
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