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Color is the most critical information for assessing histological images. However, in literature, there is no standard color space in which a particular color points are represented for computer vision tasks. In this paper, we evaluated 11 color models with three different learning schemas for their performance in classifying tumor-related colors. The color models we studied are CIELAB, CIELUV, CIEXYZ, CMY, CMYK, HSL, HSV, Hunter-LAB, NRGB, RGB, and SCT. With 11 color models, prediction accuracies of three well-known classifiers, namely SVMs, C4.5, and Naive Bayes, are statistically compared on a large dataset of 3494 Hematoxylin and Eosin (HE) stained histopathologic images. Surprisingly, experiment results show that in contrast to general assumptions, there is no single model that is better than others in every case. However, C4.5 outperformed other two classifiers by obtaining average F-measure of 0.9989. Of 11 color models, we suggest the pair of C4.5-SCT as the most accurate classification framework for tumor identification in HE stained histological images.