---
tags:
- autotrain
- tabular
- regression
- tabular-regression
datasets:
- autotrain-uzdtm-nwkp2/autotrain-data
pipeline_tag: tabular-regression
library_name: transformers
---

# Model Trained Using AutoTrain

- Problem type: Tabular regression

## Validation Metrics

- r2: 0.5287307064016351
- mse: 3.103168000915719e+19
- mae: 2243863540.8
- rmse: 5570608585.168877
- rmsle: 8.027979609819264
- loss: 5570608585.168877

## Best Params

- learning_rate: 0.11299209471906922
- reg_lambda: 1.95078305416454e-06
- reg_alpha: 0.03568550183373181
- subsample: 0.6486218191662874
- colsample_bytree: 0.22654368454464396
- max_depth: 1
- early_stopping_rounds: 481
- 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

```