Hands-On Ensemble Learning with Python

Kyriakides, George, G. Margaritis, Konstantinos

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商品描述

Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model.

 

With its hands-on approach, you'll not only get up to speed with the basic theory but also the application of different ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. In addition to this, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models.

 

By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios.

商品描述(中文翻譯)

集成學習是一種將兩個或更多相似或不相似的機器學習算法結合起來,以創建具有優越預測能力的模型的技術。本書將演示如何使用各種弱算法來構建強大的預測模型。

通過實踐方法,您不僅可以熟悉基本理論,還可以應用不同的集成學習技術。通過示例和真實世界的數據集,您將能夠生成更好的機器學習模型,以解決監督學習問題,如分類和回歸。此外,您還將利用集成學習技術,如聚類,生成無監督機器學習模型。隨著進展,各章將介紹在實際世界中廣泛使用的不同機器學習算法,以進行預測和分類。您甚至還將掌握使用Python庫,如scikit-learn和Keras,實現不同集成模型的技巧。

通過閱讀本書,您將精通集成學習,並具備了解哪種集成方法適用於哪種問題以及在實際情境中成功實施它們所需的技能。