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Dimension reduction is a process of reducing the number of variables in a dataset while keeping as much information as possible. It is common practice in machine learning as it simplifies tasks, reduces computing resource requirements and can improve accuracy by eliminating irrelevant or redundant variables.
Extra Trees Classifier (ETC) is a type of decision tree ensemble model used for classification tasks. It is similar to Random Forests in that it creates multiple decision trees and aggregates their predictions, but there are some differences in how the trees are constructed.
PCA stands for Principal Component Analysis, which is a popular technique used for dimensionality reduction and feature extraction in machine learning and data analysis. The goal of PCA is to transform a high-dimensional dataset into a lower-dimensional representation while preserving the most important information about the data.
Standardization is typically applied to numerical data that have a distribution with a mean and standard deviation. Categorical data, on the other hand, do not have these properties, so standardization is not applicable.