Table 3. List the adjusted parameters for the classifiers that were used using GridsearchCV.

Classifiers	Optimal perameters
DT	criterion = gini, max depth = 10, splitter = best, random state = 15
SVM	C = 1.0, kernel = linear, degree = 3, random state = 25
KNN	n neighbors = 5, weights = uniform, leaf size = 25, algorithm = brute
RF	n estimators = 5, random state = 20, max depth = 3, max features = sqrt
ET	n estimators = 20, criterion = gini, max depth = 7, max features = sqrt
SEHM	estimators = RF, cv = iterable, stack method = predict proba