Titlebook: Data Mining Algorithms in C++; Data Patterns and Al Timothy Masters Book 2018 Timothy Masters 2018 Data Mining.big data.algorithms.C++.prog

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Displaying Relationship Anomalies,asy to detect, see, and describe. In prior chapters we examined measures that go beyond such naiveté and are able to detect more subtle dependencies between variables, in other words, anomalies in otherwise uncomplicated relationships. But what if we want a visual representation of the pattern that

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Book 2018ationships present in your data.Discover how combinatorially symmetric cross validation reveals whether your model has true power or has just learned noise by overfitting the data.Work with feature weighting as regularized energy-based learning?to rank variables according to their predictive power w

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Book 2018-mining algorithms that are effective in a wide variety of prediction and classification applications.? All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code..Many of these techniques are recent developmen

Infusion

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f growing importanceDiscover hidden relationships among the variables in your data, and learn how to exploit these relationships.? This book presents a collection of data-mining algorithms that are effective in a wide variety of prediction and classification applications.? All algorithms include an

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Information and Entropy,find, present, and capitalize on such relationships. In this chapter, we focus primarily on a specific aspect of this task: evaluating and perhaps improving the information content of a measured variable. What is information? This term has a rigorously defined meaning, which we will now pursue.