id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_5brlhve5rrfp5k3ki4c4vyrm2i	Julian Zubek	Complexity curve: a graphical measure of data complexity and classifier performance	2016	32	.pdf	application/pdf	12468	1645	64	Keywords Learning curves, Data complexity, Data pruning, Hellinger distance, Bias-variance How to cite this article Zubek and Plewczynski (2016), Complexity curve: a graphical measure of data complexity and classifier performance. In this article, we introduce a new measure of data complexity targeted at sample The problem of measuring data complexity in the context of machine learning is A set of practical measures of data complexity with regard to classification was introduced be used as an universal measure for comparing complexity of different data sets. same setting as when calculating the complexity curve: classifiers were trained on random Figure 3 presents complexity curve and the adjusted error of decision tree classifier on Figure 7 presents conditional complexity curves for all three data sets. Table 4 Areas under conditional complexity curve (AUCC) for microarray data sets along AUC ROC values for different classifiers. complexity curves, PS to data pruning with progressive sampling.	./cache/work_5brlhve5rrfp5k3ki4c4vyrm2i.pdf	./txt/work_5brlhve5rrfp5k3ki4c4vyrm2i.txt