PATHOLOGY

Detailed explanation-1: -Linear Discriminant Analysis (LDA) This is a good choice because maximizing the distance between the means of each class when projecting the data in a lower-dimensional space can lead to better classification results (thanks to the reduced overlap between the different classes).

Detailed explanation-2: -Feature extraction increases the accuracy of learned models by extracting features from the input data. This phase of the general framework reduces the dimensionality of data by removing the redundant data. Of course, it increases training and inference speed.

Detailed explanation-3: -Feature extraction refers to the process of transforming raw data into numerical features that can be processed while preserving the information in the original data set. It yields better results than applying machine learning directly to the raw data.