egorical variables
]

---
# XGBoost
`conda install -c conda-forge xgboost`

```python
from xgboost import XGBClassifier
xgb = XGBClassifier()
xgb.fit(X_train, y_train)
xgb.score(X_test, y_test))
```

- supports missing values
- GPU training
- networked parallel training
- monotonicity constraints
- supports sparse data

???
In terms of implementation, there's a couple of
implementation methods. Scikit-learn has gradient boosting
classifier and gradient boosting regressor. Unfortunately,
they are rather slow