Conducting sparse feature selection on arbitrarily long phrases in text corpora with a focus on interpretability
Table 1: Needed C to Prune Rare Perfect Predictors.
['q', '[ITALIC] C∗', '[ITALIC] μ≈−1  [ITALIC] r=1', '[ITALIC] μ≈−1  [ITALIC] r=2', '[ITALIC] μ≈−1  [ITALIC] r=4', '[ITALIC] μ≈0  [ITALIC] r=1', '[ITALIC] μ≈0  [ITALIC] r=2', '[ITALIC] μ≈0  [ITALIC] r=4']
For this C∗, any perfect predictor of r documents will be pruned. This cutoff will generally be overly aggressive; if other predictors also predict these documents, then the gain of including the perfect predictor is potentially less. This provides a human interpretation of the impact of the regularization.