This problem becomes prominent when using a classifier, and there are not enough training examples for the regressor... Probably should investigate all the possible cases when regression fails, and what to do :) Active learning kick in.
Traceback (most recent call last):
File "/usr/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap
self.run()
File "/usr/lib/python2.7/multiprocessing/process.py", line 114, in run
self._target(_self._args, *_self._kwargs)
File "/data/MLO/regressors.py", line 137, in fit_data
gp.fit(scaled_training_set, adjusted_training_fitness)
File "/usr/local/lib/python2.7/dist-packages/scikit_learn-0.11-py2.7-linux-x86_64.egg/sklearn/gaussian_process/gaussian_process.py", line 315, in fit
"regression model size p=%d.") % (n_samples, p))
Exception: Ordinary least squares problem is undetermined n_samples=5 must be greater than the regression model size p=6.
This problem becomes prominent when using a classifier, and there are not enough training examples for the regressor... Probably should investigate all the possible cases when regression fails, and what to do :) Active learning kick in.
Traceback (most recent call last): File "/usr/lib/python2.7/multiprocessing/process.py", line 258, in _bootstrap self.run() File "/usr/lib/python2.7/multiprocessing/process.py", line 114, in run self._target(_self._args, *_self._kwargs) File "/data/MLO/regressors.py", line 137, in fit_data gp.fit(scaled_training_set, adjusted_training_fitness) File "/usr/local/lib/python2.7/dist-packages/scikit_learn-0.11-py2.7-linux-x86_64.egg/sklearn/gaussian_process/gaussian_process.py", line 315, in fit
i guess its only for square regressors