Intensive and Extensive Features in Data Science

Intensive and Extensive Features in Data Science

Intensive and Extensive Features in Data Science. Intensive variables tell us much more about a system than extensive variables.

I. Introduction

In physics, an extensive variable is one that depends on system size (like mass or volume). On the other hand, an intensive variable is one which does not depend on system size (like temperature, pressure, or density). While it may not be immediately obvious, intensive variables tell us much more about the system than extensive variables.

Comparing features based on an extensive scale is called absolute comparison. Likewise, comparing features based on an intensive scale is called** relative comparison**.

To illustrate the difference between extensive and intensive variables, let us consider two hypothetical players in the National Basketball Association (NBA) league. We shall refer to these players as Player A and Player B. Table 1 below shows the statistics for players A and B at the end of the regular season.

Image for post

Table 1. Comparing the season of two hypothetical NBA players. Image by Benjamin O. Tayo

We will also assume that Players A and B played a total of 75 and 60 games during the season, respectively. Player B played 15 games less than player A due to injuries. We will also assume that when both players are healthy, they play on average the same amount of minutes per game.

We observe from Table 1 that based on the extensive feature (Total Points), Player A performed better than Player B. Given that Total Points scored during a season is proportional to the number of games played, it makes no sense to compare players A and B based on Total Points only. A more meaningful feature is the intensive feature called points per game (PPG). We see that in terms of PPG, player B is a better scorer with 23.3 PPG compared to player A (with an average of 21.0 PPG).

covid19 features data-science machine-learning data-visualization

Bootstrap 5 Complete Course with Examples

Bootstrap 5 Tutorial - Bootstrap 5 Crash Course for Beginners

Nest.JS Tutorial for Beginners

Hello Vue 3: A First Look at Vue 3 and the Composition API

Building a simple Applications with Vue 3

Deno Crash Course: Explore Deno and Create a full REST API with Deno

How to Build a Real-time Chat App with Deno and WebSockets

Convert HTML to Markdown Online

HTML entity encoder decoder Online

15 Machine Learning and Data Science Project Ideas with Datasets

Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.

Most popular Data Science and Machine Learning courses — July 2020

Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant

Why You Should Learn R — Learn Data Science with Dataquest

Why should you learn R programming when you're aiming to learn data science? Here are six reasons why R is the right language for you.

Best Free Datasets for Data Science and Machine Learning Projects

This post will help you in finding different websites where you can easily get free Datasets to practice and develop projects in Data Science and Machine Learning.

50 Data Science Jobs That Opened Just Last Week

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.