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Singular Value Decomposition (SVD) is ubiquitous in a range of applications including computer science, economics, engineering, geology, oceanography, psychology, social networking etc. It is an unsupervised modeling technique that creates latent vectors for a subspace that reduces the dimensionality of observed data from n to k (k<<n) dimensions. Latent variables are uncorrelated variation of attribute values that are correlated in the original space. Moreover, SVD can be used to detect/remove noise/outliers, cluster similar entities and make predictions. On the other hand, classification tree is a supervised technique that accomplishes the similar tasks. It models decision trees from training data in order to make intelligent predictions. There is a close connection between SVD and decision trees, but differ in purpose, algorithm design and error analysis techniques. We present a hybrid algorithm bridges the gap between these standalone algorithms and adaptively supersedes their outcomes. For experimental analysis, we use realworld benchmark data, wines, publicly available from UCI machine learning repository. The algorithm is implemented in Matlab, supported by decision trees in Weka software, on MacOS Seirra Version 10.12.3 8GB 160MHZ.

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