We propose a method of transparent classification, named TC, which not only pursues accuracy but also clarifies cause of inaccuracy. Further, the design principles of TC ensure reproducibility11. Figure 1 shows processes of TC. In data preprocessing, TC handles missing values and mixed values. Without involving randomness and reduction, TC delivers intrinsic nature of data. In identifying distinguishable patterns, TC finds patterns from training observations, which are used for predicting which class a test observation belongs to, e.g., a malignant or benign tumor. Through increasing ratios of training to testing, TC represents the forest and the trees, and input data is given in sequence. To avoid CoD, TC finds patterns by intersecting pairwise observations in each of the classes, which possess essential features of data in miniature. In the worst case of TC, n observations produce only patterns. By contrast, KDD faces the challenge of CoD, i.e., given the lowest threshold, k items yield 2k itemsets12, and large amounts of itemsets are pruned if the threshold is high. In positive patterns (PP), TC obtains PP from positive training observations (PO). For pure PP (PPP), TC excludes any positive pattern that also appears in negative training observations (NO). By set theory, the exclusion implies none of PPP is included in any of NO and hence TC can distinguish between PO and NO. Analogously, Negative patterns (NP) and pure NP (PNP) are the counterpart of PP and PPP. Without involving either fine-tuned parameters or random selection, TC eliminates uncertainty of the methodology. In establishing the causes, TC accumulates positive, negative, and novel degrees of a test observation Ot by rule 1, 2, and 3 which associate patterns with the observation and provide obvious clues for judgement. In rule 1, Ot containing patterns in PPP gets a positive score (PS). In rule 2, Ot containing patterns in PNP gets a negative score (NS). In rule 3, Ot containing none of patterns in PPP and PNP is considered novel and gets a novelty score (NT) equal to the number of training observations. In understanding results of analytics, we evaluate performance of TC by three measures, Precision, Recall, and AUC (Area under Curve)8,12. According to the standard of diagnostic medicine13: AUC=0.5, no discrimination; 0.7≤AUC<0.8, acceptable; 0.8≤AUC<0.9, excellent; and 0.9≤AUC≤1, outstanding. In cause for prediction errors, error 1 (false positive14) occurs if Ot is predicted as positive but actually negative, denoted by NOt*. By cause 1.1, NOt* contains pure positive patterns although it should not. By cause 1.2, NOt* is novel, namely, containing no pattern in PPP and PNP. Error 2 (false negative14) occurs if Ot is predicted as negative but actually positive, denoted by POt*. By cause 2.1, POt* contains pure negative patterns but no pure positive pattern although it should. Prediction errors occur due to insufficient training data or labelling errors in training data. Increasing training data helps to reduce prediction errors. If the portion of labelling errors is small, TC has the potential of identifying labelling errors. Specifically, false negatives usually have a small NS.