Book Image

Data Science Algorithms in a Week - Second Edition

By : David Natingga
Book Image

Data Science Algorithms in a Week - Second Edition

By: David Natingga

Overview of this book

Machine learning applications are highly automated and self-modifying, and continue to improve over time with minimal human intervention, as they learn from the trained data. To address the complex nature of various real-world data problems, specialized machine learning algorithms have been developed. Through algorithmic and statistical analysis, these models can be leveraged to gain new knowledge from existing data as well. Data Science Algorithms in a Week addresses all problems related to accurate and efficient data classification and prediction. Over the course of seven days, you will be introduced to seven algorithms, along with exercises that will help you understand different aspects of machine learning. You will see how to pre-cluster your data to optimize and classify it for large datasets. This book also guides you in predicting data based on existing trends in your dataset. This book covers algorithms such as k-nearest neighbors, Naive Bayes, decision trees, random forest, k-means, regression, and time-series analysis. By the end of this book, you will understand how to choose machine learning algorithms for clustering, classification, and regression and know which is best suited for your problem
Table of Contents (16 chapters)
Title Page
Packt Upsell
Glossary of Algorithms and Methods in Data Science


In this chapter, we looked at how a decision tree ID3 algorithm first constructs a decision tree from the input data and then classifies a new data instance using the constructed tree. The decision tree was constructed by selecting the attribute for branching with the highest information gain. We studied how information gain measures the amount of information that can be learned in terms of the gain in information entropy.

We also learned that the decision tree algorithm can achieve a different result from other algorithms, such as Naive Bayes.

In the next chapter, we will learn how to combine various algorithms or classifiers into a decision forest (called random forest) in order to achieve a more accurate result.