---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- botClaiton/autotrain-data
license: pddl
---

# Model Trained Using AutoTrain

- Problem type: Tabular regression

## Validation Metrics

- r2: 0.9984848248937886
- mse: 2414.5671496869554
- mae: 25.17867390839041
- rmse: 49.13824528498098
- rmsle: 0.026803719250247764
- loss: 49.13824528498098

## Best Params

- learning_rate: 0.021447034999088264
- reg_lambda: 1.8519959907940258e-07
- reg_alpha: 0.4126490352165311
- subsample: 0.2980305940030723
- colsample_bytree: 0.9624113264792772
- max_depth: 6
- early_stopping_rounds: 213
- 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

```