Feature Variables

What is a Feature Variable in Machine Learning?

feature is a measurable property of the object you’re trying to analyze. In datasets, features appear as columns:

Feature example

The image above contains a snippet of data from a public dataset with information about passengers on the ill-fated Titanic maiden voyage. Each feature, or column, represents a measurable piece of data that can be used for analysis: Name, Age, Sex, Fare, and so on. Features are also sometimes referred to as “variables” or “attributes.” Depending on what you’re trying to analyze, the features you include in your dataset can vary widely.

Why are Feature Variables important?

Features are the basic building blocks of datasets. The quality of the features in your dataset has a major impact on the quality of the insights you will be able to get when you use that dataset for machine learning. Additionally, different business problems within the same industry do not necessarily require the same features, which is why it’s so important to have a strong understanding of the business goals of your data science project.

You can improve the quality of your dataset’s features with processes like feature selection and feature engineering, which are notoriously difficult and tedious. If these techniques are done well, you’ll have the optimal dataset with all of the essential features that might have bearing on your specific business problem, leading to the best possible model outcomes and the most beneficial insights.

Feature Variables + DataRobot

Working with features is one of the most time-consuming aspects of traditional data science. DataRobot automatically detects each feature’s data type (categorical, numerical, a date, percentage, etc.) and performs basic statistical analysis (mean, median, standard deviation, and more). Additionally, it automatically generates a histogram, frequent values chart, and table of count of occurrence for each feature, as well as providing users with the ability to manually change variable types. This allows you to quickly understand your data and what insights it could yield.

Features example 2

Not only that, DataRobot automatically performs feature selection and feature engineering, testing various combinations for each dataset to make sure the models’ results are accurate and include only the most relevant data.