Machine Learning with Spark - Second Edition

Decision trees for regression

Just like using linear models for regression tasks involves changing the loss function used, using decision trees for regression involves changing the measure of the node impurity used. The impurity metric is called variance, and is defined in the same way as the squared loss for least squares linear regression.

See the MLlib - Decision Tree section in the Spark documentation at http://spark.apache.org/docs/latest/mllib-decision-tree.html for further details on the decision tree algorithm and impurity measure for regression.

Now, we will plot a simple example of a regression problem with only one input variable shown on the x axis and the target variable on the y axis. The linear model prediction function is ...