期刊全称 | Assessing and Improving Prediction and Classification | 期刊简称 | Theory and Algorithm | 影响因子2023 | Timothy Masters | 视频video | | 发行地址 | An expert-driven practical book based on real-life assessment examples of performance and classification models.Rich with C++ code examples and analysis of data.Contains all you need to know to analyz | 图书封面 |  | 影响因子 | 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..Considerable attention is given to information theory, especially as it relates to discovering and exploiting relationships between variables employed by your models. This presentation of an often confusing subject avoids advanced mathematics, focusing instead on concepts easily understood by those with modest background in mathematics..All algorithms include an intuitive explanation of operation, essential equations, references to more rigorous theory, and commented C++ source code. Manyof these techniques are recent developments, still not in widespread use. Others are standard algorithms given a fresh look. In every case, the emphasis is on practical applicability, with all code written in such a way that it can easily be included in any program..Wh | Pindex | Book 2018 |
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