The correct answer is: D. All of the mentioned
- Training and testing data must be processed in different way
This is because the training data is used to learn the model, while the testing data is used to evaluate the model’s performance. If the training and testing data are processed in the same way, then the model may be overfitted to the training data and will not generalize well to new data.
- Test transformation would mostly be imperfect
This is because the test transformation is used to project the data into a lower-dimensional space, and this projection is not always perfect. This can lead to some loss of information, which can affect the model’s performance.
- The first goal is statistical and second is data compression in PCA
This is not entirely accurate. The first goal of PCA is to find a low-dimensional representation of the data that captures most of the variance in the data. This is a statistical goal, as it is concerned with understanding the structure of the data. The second goal of PCA is to compress the data, but this is not the primary goal.
Therefore, the correct answer is D. All of the mentioned.