Predictive Model for Diagnosis of Gestational Diabetes in the Kurdistan Region by a Combination of Clustering and Classification Algorithms: An Ensemble Approach

Table 7

Hyperparameters of classification algorithms.

	Algorithms	Hyperparameters	Description	Value
1	Decision tree	max_depth	Maximum depth of the tree	“none”
		min_samples_split	Minimum number of samples required to split an internal node	2
		min_samples_leaf	Minimum number of samples required to be at a leaf node	1
		criterion	Function to measure the quality of a split	“gini”
		max_features	Number of features to consider when looking for the best split	“sqrt '
2	Random forest	n_estimators	Number of trees in the forest.	100
		max_depth	Maximum depth of the tree	“none”
		min_samples_split	Minimum number of samples required to split an internal node	2
		min_samples_leaf	Minimum number of samples required to be at a leaf node	1
		criterion	Function to measure the quality of a split	“gini”
		max_features	Number of features to consider when looking for the best split	“sqrt”
3	SVM	C	Strength of the regularization is inversely proportional to C	1.0
		Kernel	Specifies the kernel type to be used in the algorithm	“rbf”
4	KNN	n_neighbors	Number of neighbors for decision making to classification	5
5	Logistic regression	Solver	Algorithm to use in the optimization problem	“lbfgs”
		penalty	Specify the norm of the penalty	“elasticnet”
6	Naïve Bayes	priors	Prior probabilities of the classes (depend of data)	“n_classes”
		var_smoothing	Portion of the largest variance of all features	1e-9