I was trying to train a Character Recognition Model with the help of
GradientBoostingClassifier. When i tried to run, it gave me the following
error :-
Traceback (most recent call last):
File "charRecog.py", line 23, in <module>
clf = GradientBoostingClassifier().fit(X_1,Y_1)
File
"/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/gradient_boosting.py",
line 633, in fit
return super(GradientBoostingClassifier, self).fit(X, y)
File
"/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/gradient_boosting.py",
line 439, in fit
self.init.fit(X, y)
File
"/usr/local/lib/python2.7/dist-packages/sklearn/ensemble/gradient_boosting.py",
line 85, in fit
class_counts = np.bincount(y)
ValueError: object too deep for desired array
Seeing the error, i realised it is the bincount() function which was
causing the error as it counts only the no. of occurrences of *non-negative
integers *in the array. But my labels are all strings such as 'A','e','3'
etc. This concludes that the GradientBoostingClassifier would work only
with the non-negative integer labels and hence would not be a universal
classifier. So, I believe, to fix the bug, the function bincount() should
be replaced with another function which does similar work but also
universal.
Please correct me if I'm wrong, as I am fairly new to Machine Learning.
Regards
Nikit Saraf
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