Table 6 Comparison results of algorithms... | Emerald Publishing

Table 6

Comparison results of algorithms and models

Algorithms/models	MAE	MSE	RMSE	R2 score	Accuracy
Logistic Regression (LR)	0.098	0.098	0.313	−0.155	0.902
Decision Tree (DT)	0.133	0.133	0.365	−0.565	0.867
Random Forest (RF)	0.094	0.094	0.306	−0.103	0.906
Support Vector Machines (SVM)	0.094	0.094	0.306	−0.103	0.906
Multi-Layer Perceptron (MLP)	0.208	0.097	0.312	−0.099	0.902
Recurrent Neural Network (RNN)	0.216	0.095	0.311	−0.098	0.921
Convolutional Neural Network (1D-CNN)	0.212	0.098	0.305	−0.096	0.925
XGBoost	0.225	0.148	0.387	−0.101	0.901
CatBoost	0.233	0.153	0.391	−0.100	0.902
LightGBM	0.223	0.145	0.381	−0.098	0.900

Algorithms/models	MAE	MSE	RMSE	R2 score	Accuracy
Logistic Regression (LR)	0.098	0.098	0.313	−0.155	0.902
Decision Tree (DT)	0.133	0.133	0.365	−0.565	0.867
Random Forest (RF)	0.094	0.094	0.306	−0.103	0.906
Support Vector Machines (SVM)	0.094	0.094	0.306	−0.103	0.906
Multi-Layer Perceptron (MLP)	0.208	0.097	0.312	−0.099	0.902
Recurrent Neural Network (RNN)	0.216	0.095	0.311	−0.098	0.921
Convolutional Neural Network (1D-CNN)	0.212	0.098	0.305	−0.096	0.925
XGBoost	0.225	0.148	0.387	−0.101	0.901
CatBoost	0.233	0.153	0.391	−0.100	0.902
LightGBM	0.223	0.145	0.381	−0.098	0.900