This book is aimed at analysts, data scientists, engineers, and other professionals who have an interest in generating advanced models that describe and generalize datasets of interest to them. It is assumed that the reader has basic experience of programming in Python and is familiar with elementary machine learning models. Furthermore, a basic understanding of statistics is assumed, although key points and more advanced concepts are briefly presented. Familiarity with Python's scikit-learn module would be greatly beneficial, although it is not strictly required. A standard Python installation is required. Anaconda Distribution (https://www.anaconda.com/distribution/) greatly simplifies the task of installing and managing the various Python packages, although it is not necessary. Finally, a good Integrated Development Environment (IDE) is extremely useful for managing your code and debugging. In our examples, we usually utilize the Spyder IDE, which can be easily installed through Anaconda.

You can download the example code files for this book from your account at www.packt.com. If you purchased this book elsewhere, you can visit www.packt.com/support and register to have the files emailed directly to you.

You can download the code files by following these steps:

1. Log in or register at www.packt.com.
2. Select the SUPPORT tab.
3. Click on Code Downloads & Errata.
4. Enter the name of the book in the Search box and follow the onscreen instructions.

Once the file is downloaded, please make sure that you unzip or extract the folder using the latest versions of the following:

• WinRAR/7-Zip for Windows
• Zipeg/iZip/UnRarX for macOS
• 7-Zip/PeaZip for Linux

The code bundle for the book is also hosted on GitHub at https://github.com/PacktPublishing/Hands-On-Ensemble-Learning-with-Python. In case there's an update to the code, it will be updated on the existing GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

We also provide a PDF file that has color images of the screenshots/diagrams used in this book. You can download it here: https://static.packt-cdn.com/downloads/9781789612851_ColorImages.pdf.

Visit the following link to check out videos of the code being run: http://bit.ly/2GfnRrv.

There are a number of text conventions used throughout this book.

CodeInText: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system."

A block of code is set as follows:

# --- SECTION 6 ---
# Accuracy of hard voting
print('-'*30)
print('Hard Voting:', accuracy_score(y_test, hard_predictions))

Bold: Indicates a new term, an important word, or words that you see onscreen. For example, words in menus or dialog boxes appear in the text like this. Here is an example: "Thus, the preferred approach is to utilize K-fold cross validation."