Table 2.

Advantages and disadvantages of machine learning models

Model type	Advantages	Disadvantages	Contexts of best performance
Random Forest	High accuracy, robustness to overfitting, handles both numerical and categorical data	High computational cost, less interpretable	Works well with structured data and mixed types
DCNNs	Effective with structured data, high predictive performance, good at recognizing spatial patterns	High computational requirements, needs large datasets	Suitable for image-based tasks
CapsNet	Captures spatial hierarchies, robust to variations, dynamic routing for better feature selection	Complex architecture, difficult to train, high computational resources needed	Effective for image recognition with spatial relationships
RNNs with LSTM	Handles sequential data, retains long-term dependencies, effective for time-series data	Computationally intensive, requires large training data, difficult to interpret	Best for time-series predictions and data with temporal dependencies
CNNs	Excellent at feature extraction and image analysis, scalable, handles high-dimensional data well	Requires large datasets, susceptible to overfitting if not properly regularized	Ideal for image-based tasks and spatial data