1. M

    Leave-one-out error strictly decreasing when number of parameters is increased, when it should not be?

    People may or may not be able to help intuitively shed light on my problem. Maybe I haven't considered some aspects. I'm running a non-parametric (kernel) regularized least squares estimation on some binary training data to then predict probabilistic values for the non-training data. I have 9...
  2. B

    range of tuning parameters

    Hi All, I have just started my advanced statistics study. And here is one thing I have not been able to understand for long time -- how can we pre-determine the range of testing tuning parameters in cross validation if we do not want R automatically select them? I saw some paper says using a...