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---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- gvozdev/autotrain-data-autotrain-ratings
---
# Model Trained Using AutoTrain
- Problem type: Tabular regression
## Validation Metrics
- r2: 0.004852553257630565
- mse: 1.704782407585897
- mae: 1.0301575550030646
- rmse: 1.3056731626199174
- rmsle: 0.1919556417083651
- loss: 1.3056731626199174
## Best Params
- learning_rate: 0.16113054215755473
- reg_lambda: 3.3566663737449463e-06
- reg_alpha: 1.999845686956423e-05
- subsample: 0.3521158025399591
- colsample_bytree: 0.1661721364825762
- max_depth: 2
- early_stopping_rounds: 172
- n_estimators: 20000
- 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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