Hi,
I notices spark machine learning examples use training data to validate
regression models, For instance, in linear
regression<http://spark.apache.org/docs/0.9.0/mllib-guide.html>example:
// Evaluate model on training examples and compute training errorval
valuesAndPreds = parsedData.map { point =>
val prediction = model.predict(point.features)
(point.label, prediction)}
...
Here training data was used to validated a model which was created from
the very same training data. This is just a bias estimation, and cross
validation <http://en.wikipedia.org/wiki/Cross-validation_%28statistics%29>is
missing here. In order to cross validate, we need to partition the
data
into in-sample for training, and out-of-sample for validation.
Please correct me if this does not apply to ML algorithms implemented in
spark.
