Book Image

Machine Learning Algorithms - Second Edition

Book Image

Machine Learning Algorithms - Second Edition

Overview of this book

Machine learning has gained tremendous popularity for its powerful and fast predictions with large datasets. However, the true forces behind its powerful output are the complex algorithms involving substantial statistical analysis that churn large datasets and generate substantial insight. This second edition of Machine Learning Algorithms walks you through prominent development outcomes that have taken place relating to machine learning algorithms, which constitute major contributions to the machine learning process and help you to strengthen and master statistical interpretation across the areas of supervised, semi-supervised, and reinforcement learning. Once the core concepts of an algorithm have been covered, you’ll explore real-world examples based on the most diffused libraries, such as scikit-learn, NLTK, TensorFlow, and Keras. You will discover new topics such as principal component analysis (PCA), independent component analysis (ICA), Bayesian regression, discriminant analysis, advanced clustering, and gaussian mixture. By the end of this book, you will have studied machine learning algorithms and be able to put them into production to make your machine learning applications more innovative.
Table of Contents (19 chapters)

Decision Tree classification with scikit-learn

The scikit-learn library contains the DecisionTreeClassifier class, which can train a Binary Decision Tree with Gini and cross-entropy impurity measures. In our example, let's consider a dataset with 3 features and 3 classes:

from sklearn.datasets import make_classification

nb_samples = 500

X, Y = make_classification(n_samples=nb_samples, n_features=3, n_informative=3, n_redundant=0, n_classes=3, n_clusters_per_class=1)

First, let's consider a classification with the default Gini impurity:

from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import cross_val_score

dt = DecisionTreeClassifier()
print(cross_val_score(dt, X, Y, scoring='accuracy', cv=10).mean())
0.970

A very interesting feature is given by the ability to export the tree in graphviz format and convert it into a PDF.

Graphviz is a...