<p><b>Implement machine learning algorithms to build ensemble models using Keras, H2O, Scikit-Learn, Pandas and more </b></p> <h4>Key Features</h4> <ul><li>Apply popular machine learning algorithms using a recipe-based approach </li> <li>Implement boosting, bagging, and stacking ensemble methods to
Ensemble Machine Learning Cookbook
โ Scribed by Sarkar, Dipayan.;Natarajan, Vijayalakshmi.;
- Publisher
- Packt Publishing
- Year
- 2019
- Tongue
- English
- Category
- Library
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
1 online resource (1 volume) :
<p>This book contains the extended papers presented at the 3rd Workshop on Supervised and Unsupervised Ensemble Methods <br>and their Applications (SUEMA) that was held in conjunction with the European Conference on Machine Learning and <br>Principles and Practice of Knowledge Discovery in Databases
Ensemble machine learning combines the power of multiple machine learning approaches, working together to deliver models that are highly performant and highly accurate. Inside Ensemble Methods for Machine Learning you will find: โข Methods for classification, regression, and recommendations โข So
InEnsemble Methods for Machine Learning you'll learn to implement the most important ensemble machine learning methods from scratch. Many machine learning problems are too complex to be resolved by a single model or algorithm. Ensemble machine learning trains a group of diverse machine learning m
With this book, you will learn how to perform various machine learning tasks in different environments. Youll use a wide variety of machine learning algorithms using Python to solve real-world problems. By the end of the book, you will learn to implement most used machine learning algorithms using c