Principal Component Analysis definition

1. What is principal component analysis?
2. What is principal component analysis explain with an example?
3. What does PC1 and PC2 mean?
4. What is principal component analysis in machine learning?

What is principal component analysis?

Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance.

What is principal component analysis explain with an example?

Principal Component Analysis is an unsupervised learning algorithm that is used for the dimensionality reduction in machine learning. It is a statistical process that converts the observations of correlated features into a set of linearly uncorrelated features with the help of orthogonal transformation.

What does PC1 and PC2 mean?

These axes that represent the variation are “Principal Components”, with PC1 representing the most variation in the data and PC2 representing the second most variation in the data. If we had three samples, then we would have an extra direction in which we could have variation.

What is principal component analysis in machine learning?

The Principal Component Analysis is a popular unsupervised learning technique for reducing the dimensionality of data. It increases interpretability yet, at the same time, it minimizes information loss. It helps to find the most significant features in a dataset and makes the data easy for plotting in 2D and 3D.