Exam Professional Machine Learning Engineer topic 1 question 72 discussion

but this is not a sparse input vector, just a high dimensional vector where many features are not relevant.

That is a good point. PCA is a technique used to reduce the dimensionality of the dataset by transforming the original features into a new set of uncorrelated features. This can help to eliminate the least informative features and reduce the computational burden of building a model with many input features. However, it is important to note that PCA does not necessarily remove the original features from the model, but rather transforms them into a new set of features. On the other hand, L1 regularization can effectively remove the impact of non-informative features by setting their coefficients to 0 during the model building process. Therefore, both techniques can be useful for addressing the issue of non-informative features in a linear model, depending on the specific needs of the problem.

should be A as keeping the informative ones in their original form