In this chapter, we presented the World Happiness Report data, providing a description of the data's purpose, as well as describing the data's properties. In order to gain further insights into the data, we utilized cluster analysis, leveraging ensemble techniques. We used co-occurrence matrix linkage in order to combine the cluster assignments of different base clusters. We tested various setups, with different ensemble sizes and numbers of neighbors, in order to provide a k-NN ensemble. After identifying that a t-SNE decomposition with a K value of 10 and 20 base clusters can be utilized, we analyzed the cluster assignments. We found that countries reporting the same happiness levels can, in fact, have different profiles. The most unhappy countries were, on average, developing countries who have to overcome many problems, concerning both their economies, and...
Hands-On Ensemble Learning with Python
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Hands-On Ensemble Learning with Python
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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.
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Current Title:
Hands-On Ensemble Learning with Python
Table of Contents (20 chapters)
Preface
Free Chapter
Section 1: Introduction and Required Software Tools
A Machine Learning Refresher
Getting Started with Ensemble Learning
Section 2: Non-Generative Methods
Voting
Stacking
Section 3: Generative Methods
Bagging
Boosting
Random Forests
Section 4: Clustering
Section 5: Real World Applications
Classifying Fraudulent Transactions
Predicting Bitcoin Prices
Evaluating Sentiment on Twitter
Recommending Movies with Keras
Clustering World Happiness
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