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author | Zhe Sun <ymwdalex@gmail.com> | 2017-03-03 11:55:57 +0100 |
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committer | Sean Owen <sowen@cloudera.com> | 2017-03-03 11:55:57 +0100 |
commit | 0bac3e4cde75678beac02e67b8873fe779e9ad34 (patch) | |
tree | 2e659e44dc0b96c49d45bf80753ee9121d71c157 /docs | |
parent | fa50143cd33586f4658892f434c9f6c23346e1bf (diff) | |
download | spark-0bac3e4cde75678beac02e67b8873fe779e9ad34.tar.gz spark-0bac3e4cde75678beac02e67b8873fe779e9ad34.tar.bz2 spark-0bac3e4cde75678beac02e67b8873fe779e9ad34.zip |
[SPARK-19797][DOC] ML pipeline document correction
## What changes were proposed in this pull request?
Description about pipeline in this paragraph is incorrect https://spark.apache.org/docs/latest/ml-pipeline.html#how-it-works
> If the Pipeline had more **stages**, it would call the LogisticRegressionModel’s transform() method on the DataFrame before passing the DataFrame to the next stage.
Reason: Transformer could also be a stage. But only another Estimator will invoke an transform call and pass the data to next stage. The description in the document misleads ML pipeline users.
## How was this patch tested?
This is a tiny modification of **docs/ml-pipelines.md**. I jekyll build the modification and check the compiled document.
Author: Zhe Sun <ymwdalex@gmail.com>
Closes #17137 from ymwdalex/SPARK-19797-ML-pipeline-document-correction.
Diffstat (limited to 'docs')
-rw-r--r-- | docs/ml-pipeline.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/docs/ml-pipeline.md b/docs/ml-pipeline.md index 7cbb14654e..aa92c0a37c 100644 --- a/docs/ml-pipeline.md +++ b/docs/ml-pipeline.md @@ -132,7 +132,7 @@ The `Pipeline.fit()` method is called on the original `DataFrame`, which has raw The `Tokenizer.transform()` method splits the raw text documents into words, adding a new column with words to the `DataFrame`. The `HashingTF.transform()` method converts the words column into feature vectors, adding a new column with those vectors to the `DataFrame`. Now, since `LogisticRegression` is an `Estimator`, the `Pipeline` first calls `LogisticRegression.fit()` to produce a `LogisticRegressionModel`. -If the `Pipeline` had more stages, it would call the `LogisticRegressionModel`'s `transform()` +If the `Pipeline` had more `Estimator`s, it would call the `LogisticRegressionModel`'s `transform()` method on the `DataFrame` before passing the `DataFrame` to the next stage. A `Pipeline` is an `Estimator`. |