Could you discuss the challenges presented by the curse of dimensionality and how you'd tackle them?

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Anonymous · Accepted Answer

Dimensionality problem occur because there are many correlated feature causing
model error and irrelevant feature make the model to overfitting. I would
address by reducing the dimensionality in the datasets, that said to reduce the
redundant features. I can use correlation analysis, if two features are
correlated, those are redundant features. Also, I can use L1 or L2
regularization method to reduce dimension in logistic regression or linear
regression problem. L1 regularization can remove irrelevant feature by shrinking
coefficient to zero. L2 regularization method works by reducing the feature
coefficient. Those are working by adding a penalty term in the log loss
function. If we are using the tree based method like random forest, we can do
recursive feature selection, because random forest have the feature importance
data. It works by training the model, get the feature importance data and remove
the least importance feature, recursively.

Could you discuss the challenges presented by the curse of dimensionality and how you'd tackle them?

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