tags: | |
- autotrain | |
- tabular | |
- regression | |
- tabular-regression | |
datasets: | |
- Notaspy1234/autotrain-data-Autotrain3 | |
# Model Trained Using AutoTrain | |
- Problem type: Tabular regression | |
## Validation Metrics | |
- r2: 0.9753017864826334 | |
- mse: 0.3290419495851166 | |
- mae: 0.47130432128906286 | |
- rmse: 0.5736217826975512 | |
- rmsle: 0.057378419858521094 | |
- loss: 0.5736217826975512 | |
## Best Params | |
- learning_rate: 0.022993157585548683 | |
- reg_lambda: 0.0030417803769039035 | |
- reg_alpha: 0.17755049688249555 | |
- subsample: 0.33171622212758833 | |
- colsample_bytree: 0.10545502763287017 | |
- max_depth: 8 | |
- early_stopping_rounds: 387 | |
- 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 | |
``` | |