tags: | |
- autotrain | |
- tabular | |
- regression | |
- tabular-regression | |
datasets: | |
- autotrain-uljkp-sdhgs/autotrain-data | |
# Model Trained Using AutoTrain | |
- Problem type: Tabular regression | |
## Validation Metrics | |
- r2: 0.9900762497798218 | |
- mse: 10317.805777253338 | |
- mae: 74.54517527770996 | |
- rmse: 101.57660053995377 | |
- rmsle: 0.042811727450114016 | |
- loss: 101.57660053995377 | |
## Best Params | |
- learning_rate: 0.016479102091350954 | |
- reg_lambda: 0.3449233788687026 | |
- reg_alpha: 3.244557908377455e-07 | |
- subsample: 0.5379679408548034 | |
- colsample_bytree: 0.9050706969365716 | |
- max_depth: 4 | |
- early_stopping_rounds: 293 | |
- n_estimators: 7000 | |
- 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 | |
``` | |