Github user atalwalkar commented on a diff in the pull request:
https://github.com/apache/spark/pull/1908#discussion_r16149173
--- 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 --
It does seem odd to talk about linear regression, lasso and ridge
regression as distinct algorithms, but talk about linear SVMs and logistic
regression as single algorithms and not mention different regularizers. That
being said, this is often done in practice. We could add the following
sentence to clarify this point, e.g., "...: linear support vector machine
(SVMs) and logistic regression. We support both L1 and L2 regularization for
these methods."
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