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---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- rea-xgboost/autotrain-data
---
# Model Trained Using AutoTrain
- Problem type: Tabular regression
## Validation Metrics
- r2: 0.3033866286277771
- mse: 1798692953.5327067
- mae: 31881.1203125
- rmse: 42411.00038354091
- rmsle: 0.20291934835125106
- loss: 42411.00038354091
## Best Params
- learning_rate: 0.10040353638173113
- reg_lambda: 0.006827780870976135
- reg_alpha: 0.006625264866744126
- subsample: 0.25905346245387173
- colsample_bytree: 0.2072843639904269
- max_depth: 4
- early_stopping_rounds: 122
- 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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