1. The curse of dimensionality leads to reducing dimensions and features in machine learning algorithms. (Yes | No)
Yes. The volume of data and features makes it necessary to extract the main features of an observed event (an image, sound, and words) to make sense of it.
Overfitting and underfitting apply to dimensionality reduction as well. Reducing the features until the system works in a lab (overfitting) might lead to nowhere once the application faces real-life data. Trying to use all the features might lead to underfitting because the application solves no problem at all.
Regularization applies not just to data but to every aspect of a project.
2. Transfer learning determines the profitability of a project. (Yes | No)
Yes if an application of an AI model in itself was unprofitable the first time but could generate profit...
