You can’t add apples and oranges. Dimensional analysis is essential in machine learning. In this article, we discuss the importance of dimensional analysis in machine learning.
Dimensional analysis is a technique used in physics to ensure an equation is consistent. For example, suppose we have an equation given by
Where x _is the distance (**_feet or meter) and t is time (second). For this equation to be consistent, A must have the units of distance (meter), while B must have the unit of distance/time (meter/second**).
In machine learning, we often deal with datasets containing features with different dimensions. For example, a cars dataset can have features such as length of a car (feet or meters), mass of a car (pounds or kilograms), age of a car *(years), *fuel consumption rate (miles per gallon or kilometers per liter), the color of a car (green, red, blue, black, white, etc.). Since features come in different units, it is important to be careful when performing data analysis as we can’t add apples and oranges. For example, you can’t add your mass of car *feature column to your *fuel consumption rate column, this would be a meaningless operation.
In this article, we discuss the importance of dimensional analysis in machine learning.
An overview of the oldest supervised machine-learning algorithm, its type & shortcomings.
Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding.
Linear regression is a statistical data analysis technique that helps you generate predictions for your custom data by priorly training the model on some dataset at hand.
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
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.