In PCA, what do Eigenvalues represent?
- Amount of variance explained by each component
- Direction of the new coordinate system
- Noise in the data
- Scale of the data
Eigenvalues in PCA represent the amount of variance that is accounted for by each corresponding eigenvector. The larger the eigenvalue, the more variance the principal component explains.
Loading...
Related Quiz
- To detect multicollinearity in a dataset, one common method is to calculate the ___________ Inflation Factor (VIF).
- In which scenario is unsupervised learning least suitable: predicting house prices based on features, grouping customers into segments, or classifying emails as spam or not spam?
- Multicollinearity occurs when two or more independent variables in a Multiple Linear Regression model are highly ___________.
- In a medical study, you are modeling the odds of a particular disease based on several risk factors. How would you interpret the Odds Ratio in this context?
- How does classification differ from regression in supervised learning?