Gradient boosting flavors
Ensemble Methods in Python
Román de las Heras
Data Scientist, Appodeal
Variations of gradient boosting
Gradient Boosting Algorithm
- Extreme Gradient Boosting
- Light Gradient Boosting Machine
- Categorical Boosting
Implementation
- XGBoost
- LightGBM
- CatBoost
Extreme gradient boosting (XGBoost)
Some properties:
- Optimized for distributed computing
- Parallel training by nature
- Scalable, portable, and accurate
import xgboost as xgb
clf_xgb = xgb.XGBClassifier(
n_estimators=100,
learning_rate=None,
max_depth=None,
random_state
)
clg_xgb.fit(X_train, y_train)
pred = clf_xgb.predict(X_test)
Light gradient boosting machine
Some properties:
- Released by Microsoft (2017)
- Faster training and more efficient
- Lighter in terms of space
- Optimized for parallel and GPU processing
- Useful for problems with big datasets and constraints of speed or memory
import lightgbm as lgb
clf_lgb = lgb.LGBMClassifier(
n_estimators=100,
learning_rate=0.1,
max_depth=-1,
random_state
)
clf_lgb.fit(X_train, y_train)
pred = clf_lgb.predict(X_test)
Categorical boosting
Some properties:
- Open sourced by Yandex (April 2017)
- Built-in handling of categorical features
- Accurate and robust
- Fast and scalable
- User-friendly API
import catboost as cb
clf_cat = cb.CatBoostClassifier(
n_estimators=None,
learning_rate=None,
max_depth=None,
random_state
)
clf_cat.fit(X_train, y_train)
pred = clf_cat.predict(X_test)
It's your turn!
Ensemble Methods in Python
