Can you discuss the geometric interpretation of Eigenvectors in PCA?
- They align with the mean of the data
- They define the direction of maximum variance
- They define the scaling of the data
- They represent clusters in the data
Geometrically, eigenvectors in PCA define the direction of maximum variance in the data. They are the axes along which the original data is projected, transforming it into a new coordinate system where variance is maximized.
Loading...
Related Quiz
- What are the consequences of ignoring multicollinearity in a Multiple Linear Regression model?
- What are the advantages and limitations of using Bootstrapping in Machine Learning?
- Why might it be problematic if a loan approval machine learning model is not transparent and explainable in its decision-making process?
- You are having difficulty interpreting the coefficients of your Logistic Regression model. How might the Logit function and Odds Ratio help in understanding them?
- You have built an SVM for a binary classification problem but the model is overfitting. What changes can you make to the kernel or hyperparameters to improve the model?