Deprecate all Param setter methods except for input/output column Params for `DecisionTreeClassificationModel`, `GBTClassificationModel`, `RandomForestClassificationModel`, `DecisionTreeRegressionModel`, `GBTRegressionModel` and `RandomForestRegressionModel`
Fix a bug of `ChiSqSelector` which will likely change its result. Now `ChiSquareSelector` use pValue rather than raw statistic to select a fixed number of top features.
* [SPARK-10097](https://issues.apache.org/jira/browse/SPARK-10097): `Evaluator.isLargerBetter` is
added to indicate metric ordering. Metrics like RMSE no longer flip signs as in 1.4.
## From 1.3 to 1.4
In the `spark.mllib` package, there were several breaking changes, but all in `DeveloperApi` or `Experimental` APIs:
* Gradient-Boosted Trees
* *(Breaking change)* The signature of the [`Loss.gradient`](api/scala/index.html#org.apache.spark.mllib.tree.loss.Loss) method was changed. This is only an issues for users who wrote their own losses for GBTs.
* *(Breaking change)* The `apply` and `copy` methods for the case class [`BoostingStrategy`](api/scala/index.html#org.apache.spark.mllib.tree.configuration.BoostingStrategy) have been changed because of a modification to the case class fields. This could be an issue for users who use `BoostingStrategy` to set GBT parameters.
* *(Breaking change)* The return value of [`LDA.run`](api/scala/index.html#org.apache.spark.mllib.clustering.LDA) has changed. It now returns an abstract class `LDAModel` instead of the concrete class `DistributedLDAModel`. The object of type `LDAModel` can still be cast to the appropriate concrete type, which depends on the optimization algorithm.
In the `spark.ml` package, several major API changes occurred, including:
*`Param` and other APIs for specifying parameters
*`uid` unique IDs for Pipeline components
* Reorganization of certain classes
Since the `spark.ml` API was an alpha component in Spark 1.3, we do not list all changes here.
However, since 1.4 `spark.ml` is no longer an alpha component, we will provide details on any API
changes for future releases.
## From 1.2 to 1.3
In the `spark.mllib` package, there were several breaking changes. The first change (in `ALS`) is the only one in a component not marked as Alpha or Experimental.
* *(Breaking change)* In [`ALS`](api/scala/index.html#org.apache.spark.mllib.recommendation.ALS), the extraneous method `solveLeastSquares` has been removed. The `DeveloperApi` method `analyzeBlocks` was also removed.
* *(Breaking change)* [`StandardScalerModel`](api/scala/index.html#org.apache.spark.mllib.feature.StandardScalerModel) remains an Alpha component. In it, the `variance` method has been replaced with the `std` method. To compute the column variance values returned by the original `variance` method, simply square the standard deviation values returned by `std`.
* *(Breaking change)* [`StreamingLinearRegressionWithSGD`](api/scala/index.html#org.apache.spark.mllib.regression.StreamingLinearRegressionWithSGD) remains an Experimental component. In it, there were two changes:
* The constructor taking arguments was removed in favor of a builder pattern using the default constructor plus parameter setter methods.
* Variable `model` is no longer public.
* *(Breaking change)* [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree) remains an Experimental component. In it and its associated classes, there were several changes:
* In `DecisionTree`, the deprecated class method `train` has been removed. (The object/static `train` methods remain.)
* In `Strategy`, the `checkpointDir` parameter has been removed. Checkpointing is still supported, but the checkpoint directory must be set before calling tree and tree ensemble training.
*`PythonMLlibAPI` (the interface between Scala/Java and Python for MLlib) was a public API but is now private, declared `private[python]`. This was never meant for external use.
* In linear regression (including Lasso and ridge regression), the squared loss is now divided by 2.
So in order to produce the same result as in 1.2, the regularization parameter needs to be divided by 2 and the step size needs to be multiplied by 2.
In the `spark.ml` package, the main API changes are from Spark SQL. We list the most important changes here:
* The old [SchemaRDD](https://spark.apache.org/docs/1.2.1/api/scala/index.html#org.apache.spark.sql.SchemaRDD) has been replaced with [DataFrame](api/scala/index.html#org.apache.spark.sql.DataFrame) with a somewhat modified API. All algorithms in `spark.ml` which used to use SchemaRDD now use DataFrame.
* In Spark 1.2, we used implicit conversions from `RDD`s of `LabeledPoint` into `SchemaRDD`s by calling `import sqlContext._` where `sqlContext` was an instance of `SQLContext`. These implicits have been moved, so we now call `import sqlContext.implicits._`.
* Java APIs for SQL have also changed accordingly. Please see the examples above and the [Spark SQL Programming Guide](sql-programming-guide.html) for details.
Other changes were in `LogisticRegression`:
* The `scoreCol` output column (with default value "score") was renamed to be `probabilityCol` (with default value "probability"). The type was originally `Double` (for the probability of class 1.0), but it is now `Vector` (for the probability of each class, to support multiclass classification in the future).
* In Spark 1.2, `LogisticRegressionModel` did not include an intercept. In Spark 1.3, it includes an intercept; however, it will always be 0.0 since it uses the default settings for [spark.mllib.LogisticRegressionWithLBFGS](api/scala/index.html#org.apache.spark.mllib.classification.LogisticRegressionWithLBFGS). The option to use an intercept will be added in the future.
or via [`DecisionTree`](api/scala/index.html#org.apache.spark.mllib.tree.DecisionTree)
static `trainClassifier` and `trainRegressor` methods.
2.*(Breaking change)* The API for
[`Node`](api/scala/index.html#org.apache.spark.mllib.tree.model.Node) has changed.
This should generally not affect user code, unless the user manually constructs decision trees
(instead of using the `trainClassifier` or `trainRegressor` methods).
The tree `Node` now includes more information, including the probability of the predicted label
(for classification).
3. Printing methods' output has changed. The `toString` (Scala/Java) and `__repr__` (Python) methods used to print the full model; they now print a summary. For the full model, use `toDebugString`.
Examples in the Spark distribution and examples in the
[Decision Trees Guide](mllib-decision-tree.html#examples) have been updated accordingly.