Table 2 Advantages, disadvantages and applications of each utilized model.

Advantages

(1) Recommendation systems

(2) Natural language processing

(3) Image and video analysis

(4) Fraud detection

(1) Built-in handling of categorical features

(2) Automatic handling of missing values

(3) Excellent handling of large datasets

(1) High predictive performance

(2) Efficient implementation

(3) Regularization techniques to prevent overfitting

(4) Feature importance ranking

Disadvantages

(1) Sensitive to hyperparameter tuning

(2) Prone to overfitting with complex datasets

(3) Lack of built-in handling for categorical features

(1) Longer training time for large datasets

(2) Relatively high memory consumption

(3) Requires more computational resources

(1) Requires tuning of hyperparameters

2) Limited handling of categorical features

(3) Difficult to interpret complex models

Applications

(1) Predictive modeling in various domains

(2) Financial risk analysis

(3) Healthcare and medical research

(4) Customer churn prediction

(1) Recommendation systems

(2) Natural language processing

(3) Image and video analysis

(4) Fraud detection

(1) Classification and regression problems

(2) Feature selection and ranking

(3) Anomaly detection

(4) Time series forecasting