Two-Eyed Algorithms and Problems
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Two-eyed algorithms are complex prediction algorithms that give accurate predictions and also give important insights into the structure of the data the algorithm is processing. The main example I discuss is RF/tools, a collection of algorithms for classification, regression and multiple dependent outputs. The last algorithm is a preliminary version and further progress depends on solving some fascinating questions of the characterization of dependency between variables.
An important and intriguing aspect of the classification version of RF/tools is that it can be used to analyze unsupervised data–that is, data without class labels. This conversion leads to such by-products as clustering, outlier detection, and replacement of missing data for unsupervised data.
[1] Leo Breiman,et al. Random Forests , 2001, Machine Learning.