Can someone, please, expand on this comment from the documentation (http://scikit-learn.org/stable/modules/svm.html#tips-on-practical-use):
* Using L1 penalization as provided by LinearSVC(loss='l2', penalty='l1', dual=False) yields a sparse solution, i.e. only a subset of feature weights is different from zero and contribute to the decision function. Increasing C yields a more complex model (more feature are selected). The C value that yields a "null" model (all weights equal to zero) can be calculated using l1_min_c<http://scikit-learn.org/stable/modules/generated/sklearn.svm.l1_min_c.html#sklearn.svm.l1_min_c>. Why is the number of features reduced? Thank you,
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