📄️ Feature Reduction
Feature reduction is a means through which the number of features in a dataset can be reduced. This is done by identifying the indiscriminate features and removing them from the dataset. This is done to reduce the complexity of the model and to improve the performance of the model. Feature reduction is also known as feature selection.
📄️ PCA
PCA means principal component analysis. It is a technique used to reduce the dimensionality of the dataset.
📄️ LDA
Different from PCA, LDA (Linear Discriminant Analysis) wants to make data with same label clustered together in the low dimension space. LDA assumes that the original data is classified based on the mean value, and different types of value have the same variance. Thus, LDA performs better when the original data is well separated by the mean value.