Book Image

Hands-On Ensemble Learning with Python

By : George Kyriakides, Konstantinos G. Margaritis
Book Image

Hands-On Ensemble Learning with Python

By: George Kyriakides, Konstantinos G. Margaritis

Overview of this book

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.
Table of Contents (20 chapters)
Free Chapter
1
Section 1: Introduction and Required Software Tools
4
Section 2: Non-Generative Methods
7
Section 3: Generative Methods
11
Section 4: Clustering
13
Section 5: Real World Applications

Creating the ensemble

In order to create the ensemble, we will utilize the openensembles library that we presented in Chapter 8, Clustering. As our dataset does not contain labels, we cannot use the homogeneity score in order to evaluate our clustering models. Instead, we will use the silhouette score, which evaluates how cohesive each cluster is and how separate different clusters are. First, we must load our dataset, which is provided in the WHR.csv file. The second file that we load, Regions.csv, contains the region that each country belongs to. We will utilize the data from 2017, as 2018 has a lot of missing data (for example, Delivery quality and Democratic quality are completely absent). We will fill any missing data using the median of the dataset. For our experiment, we will utilize the factors we presented earlier. We store them in the columns variable, for ease of reference...