In this chapter, we introduced Decision Trees as a particular kind of classifier. The basic idea behind this concept is that a decision process can become sequential by using splitting nodes where, according to the sample we used, a branch is chosen until we reach a final leaf. In order to build such a tree, the concept of impurity was introduced; starting from a complete dataset, our goal was to find a split point that creates two distinct sets that should share the minimum number of features and, at the end of the process, should be associated with a single target class. The complexity of a tree depends on the intrinsic purity; in other words, when it's always easy to determine a feature that best separates a set, the depth will be reduced. However, in many cases, this is almost impossible, so the resulting tree needs many intermediate nodes to reduce the impurity...
Machine Learning Algorithms - Second Edition
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.
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Current Title:
Machine Learning Algorithms - Second Edition
Table of Contents (19 chapters)
Preface
Free Chapter
A Gentle Introduction to Machine Learning
Important Elements in Machine Learning
Feature Selection and Feature Engineering
Regression Algorithms
Linear Classification Algorithms
Naive Bayes and Discriminant Analysis
Support Vector Machines
Decision Trees and Ensemble Learning
Clustering Fundamentals
Advanced Clustering
Hierarchical Clustering
Introducing Recommendation Systems
Introducing Natural Language Processing
Topic Modeling and Sentiment Analysis in NLP
Introducing Neural Networks
Advanced Deep Learning Models
Creating a Machine Learning Architecture
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