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)
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Example of label propagation

We can implement the algorithm in Python, using a test bidimensional dataset:

from sklearn.datasets import make_classification
nb_samples = 100
nb_unlabeled = 75
X, Y = make_classification(n_samples=nb_samples, n_features=2, n_informative=2, n_redundant=0, random_state=1000)
Y[Y==0] = -1
Y[nb_samples - nb_unlabeled:nb_samples] = 0

As in the other examples, we set y = 0 for all unlabeled samples (75 out of 100). The corresponding plot is shown in the following graph:

Partially labeled dataset

The dots marked with a cross are unlabeled. At this point, we can define the affinity matrix. In this case, we compute it using both methods:

from sklearn.neighbors import kneighbors_graph
nb_neighbors = 2
W_knn_sparse = kneighbors_graph(X, n_neighbors=nb_neighbors, mode='connectivity', include_self=True)
W_knn = W_knn_sparse.toarray()

The KNN matrix is obtained using the scikit-learn function kneighbors_graph...