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

Chapter 4. Random Forests

A random forest is a set of random decision trees (similar to the ones described in Chapter 3, Decision Trees), each generated on a random subset of data. A random forest classifies the features that belong to the class that is voted for by the majority of the random decision trees. Random forests tend to provide a more accurate classification of a feature than decision trees because of their decreased bias and variance.

In this chapter, we will cover the following topics:

  • The tree bagging (or bootstrap aggregation) technique as part of random forest construction, but which can also be extended to other algorithms and methods in data science in order to reduce bias and variance and, hence, improve accuracy
  • How to construct a random forest and classify a data item using a random forest constructed through the swim preference example 
  • How to implement an algorithm in Python that will construct a random forest
  • The differences between the analysis of a problem using the...