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
Contributors
Preface
Glossary of Algorithms and Methods in Data Science
Index

Summary


In this chapter, we learned that a random forest is a set of decision trees, where each tree is constructed from a sample chosen randomly from the initial data. This process is called bootstrap aggregating. Its purpose is to reduce variance and bias in classifications made by a random forest. Bias is further reduced during the construction of a decision tree by considering only a random subset of the variables for each branch of the tree.

We also learned that once a random forest is constructed, the result of the classification of a random forest is a majority vote from among all the trees in a random forest. The level of the majority also determines the level of confidence that the answer is correct.

Since random forests consist of decision trees, it is good to use them for every problem where a decision tree is a good choice. As random forests reduce the bias and variance that exists in decision tree classifiers, they outperform decision tree algorithms.

In the next chapter, we will...