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

Using random forests

Finally, we will employ a random forest ensemble. Once again, using validation curves, we will determine the optimal ensemble size. From the following graph, we conclude that 50 trees provide the least possible variance in our model, thus we proceed with ensemble size 50:

Validation curves for random forest

We provide the training and validation code as follows, as well as the achieved performance for both datasets. The following code is responsible for loading the required libraries and data, and training and evaluating the ensemble on the original and filtered datasets. We first load the required libraries and data, while creating train and test splits:

# --- SECTION 1 ---
# Libraries and data loading
import numpy as np
import pandas as pd

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.utils...