Another of the new machine learning algorithms in Oracle 20c Database is called XGBoost. Most people will have come across this algorithm due to its recent popularity with winners of Kaggle competitions and other similar events.
XGBoost is an open source software library providing a gradient boosting framework in most of the commonly used data science, machine learning and software development languages. It has it’s origins back in 2014, but the first official academic publication on the algorithm was published in 2016 by Tianqi Chen and Carlos Guestrin, from the University of Washington.
The algorithm builds upon the previous work on Decision Trees, Bagging, Random Forest, Boosting and Gradient Boosting. The benefits of using these various approaches are well know, researched, developed and proven over many years. XGBoost can be used for the typical use cases of Classification including classification, regression and ranking problems. Check out the original research paper for more details of the inner workings of the algorithm.
Regular machine learning models, like Decision Trees, simply train a single model using a training data set, and only this model is used for predictions. Although a Decision Tree is very simple to create (and very very quick to do so) its predictive power may not be as good as most other algorithms, despite providing model explainability. To overcome this limitation ensemble approaches can be used to create multiple Decision Trees and combines these for predictive purposes. Bagging is an approach where the predictions from multiple DT models are combined using majority voting. Building upon the bagging approach Random Forest uses different subsets of features and subsets of the training data, combining these in different ways to create a collection of DT models and presented as one model to the user. Boosting takes a more iterative approach to refining the models by building sequential models with each subsequent model is focused on minimizing the errors of the previous model. Gradient Boosting uses gradient descent algorithm to minimize errors in subsequent models. Finally with XGBoost builds upon these previous steps enabling parallel processing, tree pruning, missing data treatment, regularization and better cache, memory and hardware optimization. It’s commonly referred to as gradient boosting on steroids.
The following three images illustrates the differences between Decision Trees, Random Forest and XGBoost.
The XGBoost algorithm in Oracle 20c has over 40 different parameter settings, and with most scenarios the default settings with be fine for most scenarios. Only after creating a baseline model with the details will you look to explore making changes to these. Some of the typical settings include:
- Booster = gbtree
- #rounds for boosting = 10
- max_depth = 6
- num_parallel_tree = 1
- eval_metric = Classification error rate or RMSE for regression
As with most of the Oracle in-database machine learning algorithms, the setup and defining the parameters is really simple. Here is an example of minimum of parameter settings that needs to be defined.
BEGIN -- delete previous setttings DELETE FROM banking_xgb_settings; INSERT INTO BANKING_XGB_SETTINGS (setting_name, setting_value) VALUES (dbms_data_mining.algo_name, dbms_data_mining.algo_xgboost); -- For 0/1 target, choose binary:logistic as the objective. INSERT INTO BANKING_XGB_SETTINGS (setting_name, setting_value) VALUES (dbms_data_mining.xgboost_objective, 'binary:logistic’); commit; END;
To create an XGBoost model run the following. BEGIN DBMS_DATA_MINING.CREATE_MODEL ( model_name => 'BANKING_XGB_MODEL', mining_function => dbms_data_mining.classification, data_table_name => 'BANKING_72K', case_id_column_name => 'ID', target_column_name => 'TARGET', settings_table_name => 'BANKING_XGB_SETTINGS'); END;
That’s all nice and simple, as it should be, and the new model can be called in the same manner as any of the other in-database machine learning models using functions like PREDICTION, PREDICTION_PROBABILITY, etc.
One of the interesting things I found when experimenting with XGBoost was the time it took to create the completed model. Using the default settings the following table gives the time taken, in seconds to create the model.
As you can see it is VERY quick even for large data sets and gives greater predictive accuracy.