From be9cadb83fadae8099afe050ef8f99c7589e942a Mon Sep 17 00:00:00 2001 From: lemonjing <932191671@qq.com> Date: Fri, 23 Mar 2018 13:48:42 +0800 Subject: [PATCH 1/2] [MINOR][DOC] Fix ml-guide markdown typos --- docs/ml-guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/ml-guide.md b/docs/ml-guide.md index 702bcf748fc74..aea07be34cb86 100644 --- a/docs/ml-guide.md +++ b/docs/ml-guide.md @@ -111,7 +111,7 @@ and the migration guide below will explain all changes between releases. * The class and trait hierarchy for logistic regression model summaries was changed to be cleaner and better accommodate the addition of the multi-class summary. This is a breaking change for user code that casts a `LogisticRegressionTrainingSummary` to a -` BinaryLogisticRegressionTrainingSummary`. Users should instead use the `model.binarySummary` +`BinaryLogisticRegressionTrainingSummary`. Users should instead use the `model.binarySummary` method. See [SPARK-17139](https://issues.apache.org/jira/browse/SPARK-17139) for more detail (_note_ this is an `Experimental` API). This _does not_ affect the Python `summary` method, which will still work correctly for both multinomial and binary cases. From 98cd710922c6be08783948ffc90a811696a7cb62 Mon Sep 17 00:00:00 2001 From: lemonjing <932191671@qq.com> Date: Fri, 23 Mar 2018 18:13:57 +0800 Subject: [PATCH 2/2] [MINOR][DOC] Fix a few docs/mlib typos --- docs/mllib-feature-extraction.md | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/docs/mllib-feature-extraction.md b/docs/mllib-feature-extraction.md index 75aea70601875..ac61df374ccae 100644 --- a/docs/mllib-feature-extraction.md +++ b/docs/mllib-feature-extraction.md @@ -277,9 +277,7 @@ for details on the API. `ElementwiseProduct` multiplies each input vector by a provided "weight" vector, using element-wise multiplication. In other words, it scales each column of the dataset by a scalar multiplier. This represents the [Hadamard product](https://en.wikipedia.org/wiki/Hadamard_product_%28matrices%29) -between the input vector, `v` and transforming vector, `scalingVec`, to yield a result vector. -Qu8T948*1# -Denoting the `scalingVec` as "`w`," this transformation may be written as: +between the input vector, `v` and transforming vector, `scalingVec`, to yield a result vector. Denoting the `scalingVec` as "`w`", this transformation may be written as: `\[ \begin{pmatrix} v_1 \\