Elastic net uses a weighted combination of the L1 and L2 penalties that are used in both LASSO and Ridge regression, respectively.
LASSO performs feature selection by shrinking the coefficients of variables to zero
If a primary interest is to conduct automatic variable selection, only LASSO can do that.
L2, or Ridge regularization, is a form of regularization in which the penalty is based on the squared magnitude of the coefficients.
L1 regularization, or LASSO (Least Absolute Shrinkage and Selection Operator), is a kind of regularization
Regularization involves adding a penalty for complexity to the model objective function to improve a model’s generalization performance.
When the vocabulary size is small, and the binary occurrence of given words are strong features
If the documents in the corpus are of varying sizes, the larger documents are more likely to have higher word counts
The generic list of English stop words may not be appropriate if the set of documents are all related to a specific domain.
Stop words are common words that appear often throughout a set of documents but add little information