Table 5 Algorithm results Algorithm... | Emerald Publishing

Table 5

Algorithm results

Algorithm	Settings	Results
Algorithm	Settings	Training accuracy	Validation accuracy	Precision	Recall	F1-score
Adam ANN	One input layer, one hidden layer and one output layer Input layer with 7 nodes Hidden layer with 14 nodes Output layer with 3 nodes Kernel initialiser = Glorot uniform Activation function = Rectified Linear Unit (RELU) Batch size = 32 Epoch = 100. (Plateau after 40)	0.679	0.683	0.681	0.675	0.671
Random forest	Using GridSearchCV Bootstrap = True Maximum depth = 4 Maximum features = sqrt Minimum samples leaf' = 1 Minimum samples split = 2 Number of estimators = 10	0.622	0.618	0.615	0.764	0.660

Algorithm	Settings	Results
Algorithm	Settings	Training accuracy	Validation accuracy	Precision	Recall	F1-score
Adam ANN	One input layer, one hidden layer and one output layerInput layer with 7 nodesHidden layer with 14 nodesOutput layer with 3 nodesKernel initialiser = Glorot uniformActivation function = Rectified Linear Unit (RELU)Batch size = 32Epoch = 100. (Plateau after 40)	0.679	0.683	0.681	0.675	0.671
Random forest	Using GridSearchCVBootstrap = TrueMaximum depth = 4Maximum features = sqrtMinimum samples leaf' = 1Minimum samples split = 2Number of estimators = 10	0.622	0.618	0.615	0.764	0.660

Source(s): Authors own work