PCA: Principal Component Analysis

PCA (Principal Component Analysis) is an unsupervised machine learning algorithm used to reduce the dimensionality of the given data. It has first been invented by Karl Pearson (1901) and independently developed by Harold Hotelling (1933). Dimensionality reduction refers to the mapping of the original high-dimensional data onto a lower-dimensional space, thus reducing the risk of model overfitting and improving the generalization ability of the model … Continue reading PCA: Principal Component Analysis