Linear Discriminant Analysis
Linear discriminant analysis (LDA) is a dimensional reduction technique in which it is sought to maximize the variance between classes and minimizes variance within the class. In other words, the idea behind LDA is to find the projection in space that maximizes the mean between the classes and minimize the variance within the classes. This machine learning method can be used for both binary classification and multiclass classification. Similar to the previous machine learning models that have been tested, 5-fold cross-validation was used to avoid overfitting problems with the generated model. Table \ref{700354}, shows the classification performance metric based on the confusion matrix.