[SPARK-11888][ML] Decision tree persistence in spark.ml

### What changes were proposed in this pull request?

Made these MLReadable and MLWritable: DecisionTreeClassifier, DecisionTreeClassificationModel, DecisionTreeRegressor, DecisionTreeRegressionModel
* The shared implementation is in treeModels.scala
* I use case classes to create a DataFrame to save, and I use the Dataset API to parse loaded files.

Other changes:
* Made CategoricalSplit.numCategories public (to use in persistence)
* Fixed a bug in DefaultReadWriteTest.testEstimatorAndModelReadWrite, where it did not call the checkModelData function passed as an argument.  This caused an error in LDASuite, which I fixed.

### How was this patch tested?

Persistence is tested via unit tests.  For each algorithm, there are 2 non-trivial trees (depth 2).  One is built with continuous features, and one with categorical; this ensures that both types of splits are tested.

Author: Joseph K. Bradley <joseph@databricks.com>

Closes #11581 from jkbradley/dt-io.

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