How can the Eigenvalues in PCA be used to determine the significance of the corresponding Eigenvectors?
- By defining the direction of the eigenvectors
- By indicating the mean of each eigenvector
- By representing the amount of variance captured
- By showing the noise in the data
In PCA, eigenvalues are used to determine the significance of the corresponding eigenvectors by representing the amount of variance captured by each component. The larger the eigenvalue, the more significant the component.
Loading...
Related Quiz
- How does Random Forest handle missing values during the training process?
- The Gini Index in a Decision Tree aims to minimize the probability of __________.
- What is clustering in the context of Machine Learning?
- Imagine a scenario where you want to assess the stability of a statistical estimator. How would Bootstrapping help in this context?
- How can dimensionality reduction be helpful in visualizing data?