Table 3.

Parameters used in building different models for prediction and classification tasks.

ML Models	Parameter Details	Task
Linear Regression	Intercept fit: True	Prediction
(LnR)	Normalize: True
Logistic Regression	Intercept fit: True	Classification
(LgR)	Normalize: True
	Class weight: Balanced
	Regularization: L2
Multilayer Perceptron	Hidden layers: 32, 16, 4	Prediction & Classification
(MLP)	Activation: ReLU
	Optimizer: Adam
	Batch size: 128
Random Forest	Estimators: 100	Prediction & Classification
(RF)	Bootstap: True
	Maximum depth: 5
Support Vector Machine	Kernel: RBF	Prediction & Classification
(SVM)	Degree: 3
	C: 1.0
	Epsilon: 0.2