vkargov/gist:708c38e809cb1d476e39a480e7f53f19

## gistfile1.txt
In his seminal article "C4.5. Programs for Machine Learning." Quinlan uses a criterion U_CF for determining error limits in nodes of decision trees. This criterion is important as it drives the tree pruning heuristic. Problem is, no clear formula is provided in the article. However, the formula can be found in the source code for C4.5 and C5.0:
U_CF(E, N) ≡ RawExtraErrs(N, E)/N, with RawExtraErrs defined in pruning.c
To verify the article results, you can insert the following line into main() or whatever:

#define U_CF(E, N, expectation) printf("U_CF(%d, %d) = %0.3f (expected to be " #expectation ")\n", E, N, RawExtraErrs(N, E)/N)
	In his seminal article "C4.5. Programs for Machine Learning." Quinlan uses a criterion U_CF for determining error limits in nodes of decision trees. This criterion is important as it drives the tree pruning heuristic. Problem is, no clear formula is provided in the article. However, the formula can be found in the source code for C4.5 and C5.0:
	U_CF(E, N) ≡ RawExtraErrs(N, E)/N, with RawExtraErrs defined in pruning.c
	To verify the article results, you can insert the following line into main() or whatever:

	#define U_CF(E, N, expectation) printf("U_CF(%d, %d) = %0.3f (expected to be " #expectation ")\n", E, N, RawExtraErrs(N, E)/N)