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Class prediction is a supervised learning method where the algorithm learns from samples with known class membership (training set) and establishes a prediction rule to classify new samples (test set). This method can be used, for instance, to predict cancer types using genomic expression profiling. ¾Predict the class/phenotype/parameter of a sample¾Identify genes that discriminate well among classes¾Identify samples that could be potential outliers This technique is best used with at least 20 samples or conditions per class. In GeneSpring, there are two class prediction algorithms that can be used to achieve the objectives mentioned above: K-Nearest Neighbors and Support Vector Machines (SVM). This analysis guide will focus on the K-Nearest Neighbors algorithm.
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