Github user davies commented on a diff in the pull request:
https://github.com/apache/spark/pull/4564#discussion_r24648384
--- Diff: python/pyspark/ml/classification.py ---
@@ -32,22 +32,46 @@ class LogisticRegression(JavaEstimator, HasFeaturesCol,
HasLabelCol, HasPredicti
>>> from pyspark.sql import Row
>>> from pyspark.mllib.linalg import Vectors
- >>> dataset = sqlCtx.inferSchema(sc.parallelize([ \
- Row(label=1.0, features=Vectors.dense(1.0)), \
- Row(label=0.0, features=Vectors.sparse(1, [], []))]))
- >>> lr = LogisticRegression() \
- .setMaxIter(5) \
- .setRegParam(0.01)
- >>> model = lr.fit(dataset)
+ >>> df = sqlCtx.inferSchema(sc.parallelize([
--- End diff --
Wait for #4557, then you could user new API for convert RDD into DataFrame:
```
dataset = rdd.toDF()
```
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