What is Elastic-net? Why is it better in comparison to Ridge and Lasso?
Elastic net uses a weighted combination of the L1 and L2 penalties that are used in both LASSO and Ridge regression, respectively.
Data Science Interview Questions
How would you perform feature selection using Lasso?
LASSO performs feature selection by shrinking the coefficients of variables to zero
When to use Ridge Regression vs Lasso?
If a primary interest is to conduct automatic variable selection, only LASSO can do that.
What does L2 regularization (Ridge) mean?
L2, or Ridge regularization, is a form of regularization in which the penalty is based on the squared magnitude of the coefficients.
What does L1 regularization (Lasso) mean?
L1 regularization, or LASSO (Least Absolute Shrinkage and Selection Operator), is a kind of regularization
What is Regularization?
Regularization involves adding a penalty for complexity to the model objective function to improve a model’s generalization performance.
In what cases (and why) does using Binary Occurrence instead of TF-IDF makes more sense?
When the vocabulary size is small, and the binary occurrence of given words are strong features
What is Vector Normalization? How is that useful?
If the documents in the corpus are of varying sizes, the larger documents are more likely to have higher word counts
What is the problem with using a generic list of stop words?
The generic list of English stop words may not be appropriate if the set of documents are all related to a specific domain.
How to identify Stop Words?
Stop words are common words that appear often throughout a set of documents but add little information