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Ensemble Machine Learning by Ankit Dixit

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What this book covers

Chapter 1, Introduction to Ensemble Learning, is our introductory chapter to the world of ensembles. So we will see how ensembles can be useful for getting high accuracy from classifiers, and how to quantify the performance of a classifier by analyzing variance and bias errors. We will discuss three important aspects of ensemble algorithms: bagging, boosting, and stacking. We will see decision tree bagging in this chapter. We will also see how boosting works and how to use it. At the end, we will discuss what stacking is and how to implement stacked generalization.

Chapter 2, Decision Trees, teaches us about the creation of decision trees for making predictions on our dataset and how to code it in Python and then use ...

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