tags: | |
- autotrain | |
- tabular | |
- regression | |
- tabular-regression | |
datasets: | |
- autotrain-uzdtm-nwkp2/autotrain-data | |
# 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 | |
``` | |