Nowadays, machine learning theory suggests a plethora of algorithms to solve various problems such as clasification, regression, clustering etc. Hundreds of novel algorithms that outperform state-of-the-art baselines in specific domains are published each year. Wolpert and McReady’s “No Free Lunch” theorems have buried the hope that one of these algorithms may outperform all the others for an arbitrary problem. The talk attempts to answer the following two questions: Which algorithm from a predefined set will perform the best in solving a given problem?; and How can we properly compare any two algorithms if we know that they are almost exactly identical?
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