MLlib - Old Migration Guides
The migration guide for the current Spark version is kept on the MLlib Programming Guide main page.
From 1.1 to 1.2
The only API changes in MLlib v1.2 are in
which continues to be an experimental API in MLlib 1.2:
(Breaking change) The Scala API for classification takes a named argument specifying the number of classes. In MLlib v1.1, this argument was called
numClassesin Python and
numClassesForClassificationin Scala. In MLlib v1.2, the names are both set to
numClassesparameter is specified either via
(Breaking change) The API for
Nodehas changed. This should generally not affect user code, unless the user manually constructs decision trees (instead of using the
trainRegressormethods). The tree
Nodenow includes more information, including the probability of the predicted label (for classification).
Printing methods’ output has changed. The
__repr__(Python) methods used to print the full model; they now print a summary. For the full model, use
Examples in the Spark distribution and examples in the Decision Trees Guide have been updated accordingly.
From 1.0 to 1.1
The only API changes in MLlib v1.1 are in
which continues to be an experimental API in MLlib 1.1:
(Breaking change) The meaning of tree depth has been changed by 1 in order to match the implementations of trees in scikit-learn and in rpart. In MLlib v1.0, a depth-1 tree had 1 leaf node, and a depth-2 tree had 1 root node and 2 leaf nodes. In MLlib v1.1, a depth-0 tree has 1 leaf node, and a depth-1 tree has 1 root node and 2 leaf nodes. This depth is specified by the
(Non-breaking change) We recommend using the newly added
trainRegressormethods to build a
DecisionTree, rather than using the old parameter class
Strategy. These new training methods explicitly separate classification and regression, and they replace specialized parameter types with simple
Examples of the new, recommended
trainRegressor are given in the
Decision Trees Guide.
From 0.9 to 1.0
In MLlib v1.0, we support both dense and sparse input in a unified way, which introduces a few breaking changes. If your data is sparse, please store it in a sparse format instead of dense to take advantage of sparsity in both storage and computation. Details are described below.