Table 2.

Hyperparameters achieved for the machine learning models after grid search and pipelines. We can see the models, the evaluated parameters chosen for both two classes and multiclass, and their descriptions

Algorithms	Hyperparameter	Two-class	Multi-class	Description
DTC	criterion	entropy	gini	Evaluates the quality of a division
	max_depth	None	N/A	Maximum depth of the tree
	min_samples_leaf	4	2	Minimum number of samples required per leaf node
	min_samples_split	10	10	Samples needed to split an internal node
MLP	activation	logistic	relu	Function that activates the hidden layer
	hidden_layer_sizes	(100,)	(100,50)	Number of neurons of the i-th hidden layer
	learning_rate	constant	constant	Learning rate programming for weight updates
	solver	Adam	Adam	Solver for weight optimization
KNN	algorithm	auto	auto	Calculate nearest neighbors
	leaf_size	1	1	Leaf size passed to BallTree or KDTree
	n_neighbors	1	1	Number of neighbors
	p	2	1	Indicates the power for the Minkowski metric
	weights	‘uniform’	‘uniform’	Used in prediction
SGDC	alpha	0.001	0.01	Constant that multiplies the regularization term
	loss	‘hinge’	‘hinge’
	max_iter	2000	1000	Number of epochs performed on training data
	penalty	‘l2’	‘l1’	Regularization term
	weights	‘uniform’	‘uniform’	Used in prediction
ETC	min_samples_split	4	N/A	Samples needed to split an internal node
	n_estimators	150	300	Number of trees in the forest
	random_state	20	20	Controls the bootstrapping of the samples
	weights	‘uniform’	‘uniform’	Used in prediction
SVM	C	10	10	Regularization parameter
	gamma	‘scale’	‘auto’	Is a coefficient
	kernel	‘rbf’	‘rbf’	Is the type of kernel in use
RFC	max_depth	None	None	Maximum depth of the tree
	min_samples_split	2	2	Samples needed to split an internal node
	n_estimators	500	500	Number of trees in the forest
	random_state	40	40	Controls the bootstrapping of the samples
GB	learning_rate	0.1	0.1	Is a compensation between n_estimators and learning_rate
	max_depth	5	7	Maximum depth of the regression estimators
	n_estimators	200	200	The number of stages to be performed
	random_state	40	10	At each iteration of reinforcement controls the seed