Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1908#discussion_r16149641
--- Diff: docs/mllib-linear-methods.md ---
@@ -106,27 +105,25 @@ Here `$\mathrm{sign}(\wv)$` is the vector consisting
of the signs (`$\pm1$`) of
of `$\wv$`.
L2-regularized problems are generally easier to solve than L1-regularized
due to smoothness.
-However, L1 regularization can help promote sparsity in weights, leading
to simpler models, which is
-also used for feature selection. It is not recommended to train models
without any regularization,
+However, L1 regularization can help promote sparsity in weights leading to
smaller and more interpretable models, the latter of which can be useful for
feature selection.
+It is not recommended to train models without any regularization,
especially when the number of training examples is small.
## Binary classification
-[Binary
classification](http://en.wikipedia.org/wiki/Binary_classification) is to
divide items into
+[Binary
classification](http://en.wikipedia.org/wiki/Binary_classification) aims to
divide items into
two categories: positive and negative. MLlib supports two linear methods
for binary classification:
-linear support vector machine (SVM) and logistic regression. The training
data set is represented
+linear support vector machines (SVMs) and logistic regression. The
training data set is represented
--- End diff --
That's an interesting point. For linear regression, people created new
names for different types of regularization, some of which even shadowed the
original name. It would be nice to add a sentence to clarify that we are
counting different regularization types.
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