A statistician is currently running a gradient boosting machine on a large healthcare dataset with approximately 700,000 records and over 600 variables, most of which are sparse binary. The goal is to predict a binary outcome. The process, initiated on a work laptop, has been running for over 13 hours. The hyperparameters chosen for this task include 5,000 iterations and a shrinkage parameter of 0.001. The dataset was partitioned into training and testing sets with an 80-20 split. The model uses 5-fold cross-validation, and the tuning grid includes interaction depths of 1, 2, and 4, with minimum observations in nodes set at 5, 10, and 15. The long runtime raises questions about the feasibility of these settings for the given dataset size.
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