diff options
| author | DB Tsai <dbtsai@alpinenow.com> | 2015-03-02 22:37:12 -0800 |
|---|---|---|
| committer | Xiangrui Meng <meng@databricks.com> | 2015-03-02 22:37:12 -0800 |
| commit | b196056190c569505cc32669d1aec30ed9d70665 (patch) | |
| tree | 1a3c9f781ecc8af7b273bf042decd706c2af02f1 /docs | |
| parent | c2fe3a6ff1a48a9da54d2c2c4d80ecd06cdeebca (diff) | |
| download | spark-b196056190c569505cc32669d1aec30ed9d70665.tar.gz spark-b196056190c569505cc32669d1aec30ed9d70665.tar.bz2 spark-b196056190c569505cc32669d1aec30ed9d70665.zip | |
[SPARK-5537][MLlib][Docs] Add user guide for multinomial logistic regression
Adding more description on top of #4861.
Author: DB Tsai <dbtsai@alpinenow.com>
Closes #4866 from dbtsai/doc and squashes the following commits:
37e9d07 [DB Tsai] doc
Diffstat (limited to 'docs')
| -rw-r--r-- | docs/mllib-linear-methods.md | 10 |
1 files changed, 10 insertions, 0 deletions
diff --git a/docs/mllib-linear-methods.md b/docs/mllib-linear-methods.md index 03f90d718a..9270741d43 100644 --- a/docs/mllib-linear-methods.md +++ b/docs/mllib-linear-methods.md @@ -784,9 +784,19 @@ 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) |