Assessing and Improving Prediction and Classification: Theory and Algorithms in C++-P2P
Assess the quality of your prediction and classification models in ways that accurately reflect their real-world performance, and then improve this performance using state-of-the-art algorithms such as committee-based decision making, resampling the dataset, and boosting. This book presents many important techniques for building powerful, robust models and quantifying their expected behavior when put to work in your application.


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Assessing and Improving Prediction and Classification: Theory and Algorithms in C++-P2P
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Assessing and Improving Prediction and Classification: Theory and Algorithms in C++-P2P
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