Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/8260#discussion_r37255402
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/package.scala ---
@@ -0,0 +1,89 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements. See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License. You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.ml
+
+import org.apache.spark.ml.feature.{HashingTF, IDF, IDFModel,
VectorAssembler}
+import org.apache.spark.sql.DataFrame
+
+/**
+ * == Feature transformers ==
+ *
+ * The `ml.feature` package provides common feature transformers that help
convert raw data or
+ * features into more suitable forms for model fitting.
+ * Most feature transformers are implemented as [[Transformer]]s, which
transform one [[DataFrame]]
+ * into another, e.g., [[HashingTF]].
+ * Some feature transformers are implemented as [[Estimator]]s, because
the transformation requires
+ * some aggregated information of the dataset, e.g., document frequencies
in [[IDF]].
+ * For those feature transformers, calling [[Estimator!.fit]] is required
to obtain the model first,
+ * e.g., [[IDFModel]], in order to apply transformation.
+ * The transformation is usually done by appending new columns to the
input [[DataFrame]], so all
+ * input columns are carried over.
+ *
+ * We try to make each transformer minimal, so it becomes flexible to
assemble feature
+ * transformation pipelines.
+ * [[Pipeline]] can be used to chain feature transformers, and
[[VectorAssembler]] can be used to
+ * combine multiple feature transformations, for example:
+ *
+ * {{{
+ * import org.apache.spark.ml.feature._
+ * import org.apache.spark.ml.Pipeline
+ *
+ * // a DataFrame with three columns: id (integer), text (string), and
rating (double).
+ * val df = sqlContext.createDataFrame(Seq(
+ * (0, "Hi I heard about Spark", 3.0),
+ * (1, "I wish Java could use case classes", 4.0),
+ * (2, "Logistic regression models are neat", 4.0)
+ * )).toDF("id", "text", "rating")
+ *
+ * // define feature transformers
+ * val tok = new RegexTokenizer()
+ * .setInputCol("text")
+ * .setOutputCol("words")
+ * val sw = new StopWordsRemover()
+ * .setInputCol("words")
+ * .setOutputCol("filtered_words")
+ * val tf = new HashingTF()
+ * .setInputCol("filtered_words")
+ * .setOutputCol("tf")
+ * .setNumFeatures(10000)
+ * val idf = new IDF()
+ * .setInputCol("tf")
+ * .setOutputCol("tf_idf")
+ * val assembler = new VectorAssembler()
+ * .setInputCols(Array("tf_idf", "rating"))
+ * .setOutputCol("features")
+ *
+ * // assemble and fit the feature transformation pipeline
+ * val pipeline = new Pipeline()
+ * .setStages(Array(tok, sw, tf, idf, assembler))
+ * val model = pipeline.fit(df)
+ *
+ * // save transformed features with raw data
+ * model.transform(df)
+ * .select("id", "text", "rating", "features")
+ * .write.format("parquet").save("/output/path")
+ * }}}
+ *
+ * Some feature transformers implemented in MLlib are inspired by those
implemented in scikit-learn.
+ * The major difference is that most scikit-learn's feature transformers
operate eagerly on the
--- End diff --
"most scikit-learn's" --> "most scikit-learn"
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