Re: Cross validation is missing in machine learning examples

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-iterat

Cross validation is missing in machine learning examples

Hi, I notices spark machine learning examples use training data to validate regression models, For instance, in linear regressionexample: // Evaluate model on training examples and compute training errorval valuesAndPreds = parsedData.map { poi