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[SPARK-5537][MLlib][Docs] Add user guide for multinomial logistic regression #4866

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[SPARK-5537][MLlib][Docs] Add user guide for multinomial logistic regression #4866

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10 changes: 10 additions & 0 deletions docs/mllib-linear-methods.md
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Expand Up @@ -628,9 +628,19 @@ regularization parameter (`regParam`) along with various parameters associated w
gradient descent (`stepSize`, `numIterations`, `miniBatchFraction`). For each of them, we support
all three possible regularizations (none, L1 or L2).

For Logistic Regression, [L-BFGS](api/scala/index.html#org.apache.spark.mllib.optimization.LBFGS)
version is implemented under [LogisticRegressionWithLBFGS]
(api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS), and this
version supports both binary and multinomial Logistic Regression while SGD version only supports
binary Logistic Regression. However, L-BFGS version doesn't support L1 regularization but SGD one
supports L1 regularization. When L1 regularization is not required, L-BFGS version is strongly
recommended since it converges faster and more accurately compared to SGD by approximating the
inverse Hessian matrix using quasi-Newton method.

Algorithms are all implemented in Scala:

* [SVMWithSGD](api/scala/index.html#org.apache.spark.mllib.classification.SVMWithSGD)
* [LogisticRegressionWithLBFGS](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS)
* [LogisticRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithSGD)
* [LinearRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.regression.LinearRegressionWithSGD)
* [RidgeRegressionWithSGD](api/scala/index.html#org.apache.spark.mllib.regression.RidgeRegressionWithSGD)
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