#### 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
Title Page
Packt Upsell
Contributors
Preface
Free Chapter
Classification Using K-Nearest Neighbors
Time Series Analysis
Python Reference
Statistics
Glossary of Algorithms and Methods in Data Science
Other Books You May Enjoy
Index

## Swim preference – analysis involving a random forest

We will use the example from Chapter 3Decision Trees concerning swim preferences. We have the same data table, as follows:

 Swimming suit Water temperature Swim preference None Cold No None Warm No Small Cold No Small Warm No Good Cold No Good Warm Yes

We would like to construct a random forest from this data and use it to classify an item `(Good,Cold,?)`.

### Analysis

We are given M=3 variables, according to which a feature can be classified. In a random forest algorithm, we usually do not use all three variables to form tree branches at each node. We only use a subset (m) of variables from M. So we choose m such that m is less than, or equal to, M. The greater m is, the stronger the classifier is in each constructed tree. However, as mentioned earlier, more data leads to more bias. But, because we use multiple trees (with a lower m), even if each constructed tree is a weak classifier, their combined classification accuracy is strong. As we want to reduce bias in...