Big Data Analysis using ensemble machine learning of scikit-learn in Python - Couverture souple

Takefuji, Yoshiyasu

9781521530726: Big Data Analysis using ensemble machine learning of scikit-learn in Python

Couverture souple

ISBN 10 : 1521530726 ISBN 13 : 9781521530726

Editeur : Independently published, 2017

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This book illustrates how to implement ensemble machine learning programs in Python using scikit-learn which is one of the best open source machine learning libraries for businessman or businesswoman. This book is suitable to the interested layman who would like to analyze big data without serious programming in Python. Three examples are illustrated how to use ensemble machine learning for big data analysis. In the first example, rules between poker hands (five cards) and their rankings can be trained using ensemble machine learning. In the second example, the conventional multiple regression method is compared with the ensemble machine learning by using ice cream sales with data of the daily high temperature and the number of passers-by. In the last example, Artificial wine sommelier is built by using red wine quality data. The illustrated ensemble machine learning includes AdaBoost, Bagging, ExtraTrees, GradientBoosting, and RandomForest. The grid-search example is also illustrated. Author published four online eLetters in Science (journal) on machine learning: 1. Ensemble methods significantly improve prediction, Science (eLetter, 12 April 2017) http://science.sciencemag.org/content/355/6324/515/tab-e-letters 2. Statistical syllogism and deductive syllogism in software packages, Science (eLetter,12 April 2017) http://science.sciencemag.org/content/355/6324/468/tab-e-letters 3. Inductive and deductive reasoning must be merged for enhancing prediction and breaking its limits, Science (eLetter, 8 April 2017) http://science.sciencemag.org/content/355/6324/468/tab-e-letters 4. Ensemble methods can improve election prediction, Science (eLetter, 7 April 2017) http://science.sciencemag.org/content/355/6324/515/tab-e-letters

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