ssifier()
lgbm.fit(X_train, y_train)
lgbm.score(X_test, y_test))
```

- supports missing values
- natively supports categorical variables
- GPU training
- networked parallel training
- monotonicity constraints
- supports sparse data
---
# CatBoost
.smallest[
`conda install -c conda-forge catboost`
```python
from catboost.sklearn import CatBoostClassifier
catb = CatBoostClassifier()
catb.fit(X_train, y_train)
catb.score(X_test, y_test))
```
]
.smaller[
- optimized for categorical variables
- uses one feature