Aureliano, you're correct that this is not validation error, which is
computed as the residuals on out-of-training-sample data, and helps
minimize overfit variance.
However, in this example, the errors are correctly referred to as training
error, which is what you might compute on a per-iteration
Hi,
I notices spark machine learning examples use training data to validate
regression models, For instance, in linear
regressionhttp://spark.apache.org/docs/0.9.0/mllib-guide.htmlexample:
// Evaluate model on training examples and compute training errorval
valuesAndPreds = parsedData.map {
