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| -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`. |