An Approach for Demand Forecasting in Steel Industries Using Ensemble Learning

Table 5

Best-performing ML model and preprocessing and tuned hyperparameters with the highest possible accuracy .

ML techniques	Best preprocessing	Best hyperparameters	Performance
SVR	Processed data Corr (n_attributes: 4)
		Kernel: RBF
		Epsilon: 0.1
		Gamma: 0.001
RFR	Processed data PCA (n_attributes: 4)	n_estimators: 10
		Criterion: mse
		max_depth: 8
		min_samples_leaf: 2
		min_samples_split: 2
MLP	Processed data PCA (n_attributes: 4)	initial_learning_rate: 0.01
		Solver: Adam
		learning_rate_adjustment: Constant
		hidden_layer_sizes: (12, 12, 12, 8)
		activation_functions: relu
		Alpha ( penalty): 0.01
ELM	Processed data Corr (n_attributes: 4)	n_neurons: (8)
		activation_functions: relu
		Alpha: 100
GBR	Processed data Corr (n_attributes: 4)	n_estimators: 250
		max_features: Sqrt
		min_samples_leaf: 2
		max_depth: 2
		learning_rate: 0.2
		Loss: lad
XGBR	Processed data Corr (n_attributes: 4)	cosample_bytree
		Subsample
		reg_lamda
		reg_alpha
		min_child_weight
		learning_rate
STACK 1	Processed data Corr (n_attributes: 4)	100 kernel: RBF
STACK 2	Processed data Corr (n_attributes: 4)	Alpha ( penalty): 0.001