Book Image

The Python Workshop

By : Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade
Book Image

The Python Workshop

By: Olivier Pons, Andrew Bird, Dr. Lau Cher Han, Mario Corchero Jiménez, Graham Lee, Corey Wade

Overview of this book

Have you always wanted to learn Python, but never quite known how to start? More applications than we realize are being developed using Python because it is easy to learn, read, and write. You can now start learning the language quickly and effectively with the help of this interactive tutorial. The Python Workshop starts by showing you how to correctly apply Python syntax to write simple programs, and how to use appropriate Python structures to store and retrieve data. You'll see how to handle files, deal with errors, and use classes and methods to write concise, reusable, and efficient code. As you advance, you'll understand how to use the standard library, debug code to troubleshoot problems, and write unit tests to validate application behavior. You'll gain insights into using the pandas and NumPy libraries for analyzing data, and the graphical libraries of Matplotlib and Seaborn to create impactful data visualizations. By focusing on entry-level data science, you'll build your practical Python skills in a way that mirrors real-world development. Finally, you'll discover the key steps in building and using simple machine learning algorithms. By the end of this Python book, you'll have the knowledge, skills and confidence to creatively tackle your own ambitious projects with Python.
Table of Contents (13 chapters)

Boosting Methods

Random Forests are a type of bagging method. A bagging method is a machine learning method that aggregates a large sum of machine learning models. In the case of Random Forests, the aggregates are decision trees.

Another machine learning method is boosting. The idea behind boosting is to transform a weak learner into a strong learner by modifying the weights for the rows that the learner got wrong. A weak learner may have an error of 49%, hardly better than a coin flip. A strong learner, by contrast, may have an error rate of 1 or 2 %. With enough iterations, very weak learners can be transformed into very strong learners.

The success of boosting methods caught the attention of the machine learning community. In 2003, Yoav Fruend and Robert Shapire won the 2003 Godel Prize for developing AdaBoost, short for adaptive boosting.

Like many boosting methods, AdaBoost has both a classifier and a regressor. AdaBoost adjusts weak learners toward instances that were...