Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes

Table 3 Summary of the values of the hyperparameters for the best random forest, classification and regression tree, Naïve Byer’s classifier, eXtreme gradient boosting

Methods	Hyperparameters	Best value	Meaning
RF	Mtry	8	The number of random features used in each tree
	Ntree	500	The number of trees in forest
CART	Minispilt	20	The minimum number of observations required to attempt a split in a node
	Minibucket	7	The minimum number of observations in a terminal node
	Maxdepth	10	The maximum depth of any node in the final tree
	Xval	10	Number of cross-validations
	Cp	0.03588	Complexity parameter: The minimum improvement required in the model at each node
XGBoost	Nrounds	100	The number of tree model iterations
	Max_depth	3	The maximum depth of a tree
	Eta	0.4	Shrinkage coefficient of tree
	Gamma	0	The minimum loss reduction
	Subsample	0.75	Subsample ratio of columns when building each tree
	Colsample_bytree	0.8	Subsample ratio of columns when constructing each tree
	Rate_drop	0.5	Rate of trees dropped
	Skip_drop	0.05	Probability of skipping the dropout procedure during a boosting iteration
	Min_child_weight	1	The minimum sum of instance weight
NB	Fl	0	Adjustment of Laplace smoother
	Usekernel	TRUE	Using kernel density estimate for continuous variable versus a Gaussian density estimate
	Adjust	1	Adjust the bandwidth of the kernel density

CART: Classification and regression tree; RF: random forest; XGBoost: eXtreme gradient boosting; NB: Naïve Byes.