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.

It cannot be A either because PCA modifies the features, and it says you should keep them in their original form. and D cannot be because again dropout is for generalizing and avoiding overfitting, and it's done on the NN model not on the data.

Its wrong. Using Shapley values to determine feature importance can be a useful technique, but it requires building a complete model and can be computationally expensive, especially with over 100 input features. Additionally, it may not be practical to use this method for every model iteration or update. On the other hand, L1 regularization can be used during the model building process to effectively reduce the impact of non-informative features by shrinking their coefficients to 0, making it a more efficient and effective approach.

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