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XGBoost & LightGBM

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Gabriel Cypriano Saca

This document summarizes gradient boosting algorithms XGBoost and LightGBM. It covers decision trees, overfitting, regularization, feature engineering, parameter tuning, evaluation metrics, and comparisons between XGBoost and LightGBM. Key aspects discussed include XGBoost and LightGBM's tolerance of outliers, non-standardized features, collinear features, and NaN values. Parameter tuning, using RandomizedSearchCV and GridSearchCV, and ensembling models to optimize multiple metrics are also covered.

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Gradient Boosting in Practice: XGBoost and LightGBM Gabriel Cypriano
source: http://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting
Decision Trees review source: https://intelligentjava.wordpress.com/2015/04/28/machine-learning-decision-tree
Overfitting review source: https://en.wikipedia.org/wiki/Overfitting
Overfitting with Decision Trees
Gradient Boosting source: https://blog.bigml.com/2017/03/14/introduction-to-boosted-trees
Regularization review source: https://www.r-bloggers.com/an-attempt-to-understand-boosting-algorithms
Regularization review Ridge (L2) Lasso (L1)
XGBoost
Feature Engineering ● OK with outliers ● OK with non-standardized features ● OK with collinear features ● OK with NaN values or lack thereof
Feature Engineering ● Got NaN’s? ○ set them to -999 ○ set missing = -999 or lack thereof
XGBoost Parameter Tuning ● n_estimators ● max_depth ● learning_rate ● reg_lambda ● reg_alpha ● subsample ● colsample_bytree ● gamma yes, it’s combinatorial
XGBoost Parameter Tuning RandomizedSearchCV and GridSearchCV to the rescue.
XGBoost Parameter Tuning How not to do grid search (3 * 2 * 15 * 3 = 270 models):
XGBoost Parameter Tuning 1 * 1 * 3 * 5 * 3 = 45 models
Evaluation Metric
XGBoost Ensembles VotingClassifier with voting='soft' for combining multiple XGBoost models and optimizing for multiple metrics. ensemble xgb_auc xgb_precision xgb_log_loss AUC 0.84 0.84 0.76 0.84 Precision 0.71 0.44 0.94 0.71 Log Loss 0.42 0.49 0.48 0.38
LightGBM
LightGBM and its advantages ● OK with NaN values ● OK with categorical features ● Faster training than XGBoost ● Often better results
Trees: Categorical features vs One-Hot-Encoded features source: https://medium.com/data-design/visiting-catego rical-features-and-encoding-in-decision-trees-534 00fa65931
Tree Growth strategies XGBoost: LightGBM: source: https://www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vs-xgboost
The good ● Often yields good results ● Reduced need for feature engineering ● Fast to train a single model ● Good choice if all you have is 1 shot at the problem ● GPU support ● Scikit-learn API ● Great to ensemble and optimize for multiple metrics The bad ● Too many parameters ● Slow to tune parameters ● GPU config can be tough (try Docker) ● No GPU support on scikit-learn API (XGBoost)
Gracias! gabrielcs.me vagas.creditas.com.br somostera.com

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