Leave-one-out Cross-validation

I am trying to evaluate a multivariable dataset by leave-one-out cross-validation and then remove those samples not predictive of the original dataset (Benjamini-corrected, FDR >

Solution 1:

cross_validation.LeaveOneOut(x.shape[0]) is creating as many folds as the number of rows. This results in each validation run getting only one instance.

Now, to draw a "line" you need two points, whereas with your one instance, you only have one point. That's what your error message says, that it needs more than one instance (or sample) to draw the "line" that will be used to calculate the r^2 value.

Generally, in the ML world, people report 10-fold or 5-fold cross validation result. So I would recommend setting the n to 10 or 5, accordingly.

Edit: After a quick discussion with @banana, we realized that the question was not understood correctly initially. Since it is not possible to get the R2 score for a single data point, an alternative is to calculate the distance between the actual and predicted points. This can be done using numpy.linalg.norm(clf.predict(x[test_i])[0] - y[test_i])