Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/1908#discussion_r16143694
--- 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 --
How many linear SVMs do you count here? Does linear SVM with different
regularization count as different SVMs?
---
If your project is set up for it, you can reply to this email and have your
reply appear on GitHub as well. If your project does not have this feature
enabled and wishes so, or if the feature is enabled but not working, please
contact infrastructure at [email protected] or file a JIRA ticket
with INFRA.
---
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
