Book Image

Mastering Machine Learning Algorithms. - Second Edition

By : Giuseppe Bonaccorso
Book Image

Mastering Machine Learning Algorithms. - Second Edition

By: Giuseppe Bonaccorso

Overview of this book

Mastering Machine Learning Algorithms, Second Edition helps you harness the real power of machine learning algorithms in order to implement smarter ways of meeting today's overwhelming data needs. This newly updated and revised guide will help you master algorithms used widely in semi-supervised learning, reinforcement learning, supervised learning, and unsupervised learning domains. You will use all the modern libraries from the Python ecosystem – including NumPy and Keras – to extract features from varied complexities of data. Ranging from Bayesian models to the Markov chain Monte Carlo algorithm to Hidden Markov models, this machine learning book teaches you how to extract features from your dataset, perform complex dimensionality reduction, and train supervised and semi-supervised models by making use of Python-based libraries such as scikit-learn. You will also discover practical applications for complex techniques such as maximum likelihood estimation, Hebbian learning, and ensemble learning, and how to use TensorFlow 2.x to train effective deep neural networks. By the end of this book, you will be ready to implement and solve end-to-end machine learning problems and use case scenarios.
Table of Contents (28 chapters)
26
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27
Index

Gradient boosting

At this point, we can introduce a more general method of creating boosted ensembles. Let's choose a generic algorithm family, represented as follows:

Each model is parametrized using the vector and there are no restrictions on the kind of method that is employed. In this case, we are going to consider decision trees (which is one of the most diffused algorithms when this boosting strategy is employed—in this case, the algorithm is known as gradient tree boosting), but the theory is generic and can be easily applied to more complex models, such as neural networks. In a decision tree, the parameter vector is made up of selection tuples, so the reader can think of this method as a pseudo-random forest where, instead of randomness, we look for extra optimality exploiting the previous experience. In fact, as with AdaBoost, a gradient boosting ensemble is built sequentially, using a technique that is formally defined as Forward Stage-wise Additive Modeling...