---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- autotrain-vessel-eta/autotrain-data
---

# Model Trained Using AutoTrain

- Problem type: Tabular regression

## Validation Metrics

- r2: 0.2033836841583252
- mse: 53092.500978994
- mae: 150.96381290340423
- rmse: 230.41810037189788
- rmsle: 0.9569819523414094
- loss: 230.41810037189788

## Best Params

- learning_rate: 0.0695392185390836
- reg_lambda: 0.00017542491817558795
- reg_alpha: 0.6577124531542021
- subsample: 0.3632574815242663
- colsample_bytree: 0.8007491192913739
- max_depth: 4
- early_stopping_rounds: 166
- 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

```