Price: $43.99 - $28.92
(as of Dec 13,2024 23:30:39 UTC – Details)

Publisher ‏ : ‎ Packt Publishing (July 24, 2019)
Language ‏ : ‎ English
Paperback ‏ : ‎ 298 pages
ISBN-10 ‏ : ‎ 1789612853
ISBN-13 ‏ : ‎ 978-1789612851
Item Weight ‏ : ‎ 1.14 pounds
Dimensions ‏ : ‎ 9.25 x 7.52 x 0.63 inches

In this post, we will delve into the world of ensemble learning with Python. Ensemble learning is a powerful machine learning technique that combines multiple models to improve predictive performance. By leveraging the diversity of different models, ensemble learning can often outperform individual models and produce more accurate results.

In this hands-on tutorial, we will explore popular ensemble learning techniques such as bagging, boosting, and stacking. We will use Python, along with popular libraries such as scikit-learn, to implement these techniques and apply them to real-world datasets.

Throughout this tutorial, we will cover the following topics:

1. Introduction to ensemble learning and its benefits
2. Bagging techniques such as Random Forest and Extra Trees
3. Boosting techniques such as AdaBoost and Gradient Boosting
4. Stacking, a meta-ensemble technique that combines multiple models
5. Implementing ensemble learning models in Python using scikit-learn

By the end of this tutorial, you will have a solid understanding of ensemble learning techniques and how to implement them in Python. So grab your laptop, fire up your Python IDE, and let’s dive into the world of ensemble learning!
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