---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- autotrain-dn6m8-0r8r6/autotrain-data
---

# Model Trained Using AutoTrain

- Problem type: Tabular regression

## Validation Metrics

- r2: 0.9984788500175956
- mse: 2424.0886496905105
- mae: 26.34647989435065
- rmse: 49.23503477901189
- rmsle: 0.028818836691457343
- loss: 49.23503477901189

## Best Params

- learning_rate: 0.12077471502182306
- reg_lambda: 1.105329890230882e-08
- reg_alpha: 1.4774392499746047
- subsample: 0.73978922085205
- colsample_bytree: 0.8233279668396214
- max_depth: 4
- early_stopping_rounds: 243
- 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

```