Everything You Need to Know About Data Science Feature Engineering

Having trouble understanding what exactly is feature engineering? No worries you are in the right place. This blog post contains useful information that is sure to help you remove any doubts you may have about Data Science Feature Engineering. So read this article thoroughly so you have a clear understanding of feature engineering.

Data Science Feature Engineering

What Exactly is Feature Engineering in Machine Learning?

Feature engineering is the procedure that involves taking raw data and transforming it into features, which can be used to form a predictive model using statistical modeling or machine learning. Put simply, it is the process by which new input features are created for machine learning. Features are taken from the raw data and later transformed into formats that are compatible with the machine learning process.

The objective of feature engineering is to prepare the set of input data that best suits machine learning algorithms to improve the efficiency and accuracy of the machine learning models. It can also be of help to data scientists in extracting more variables from data. Automated feature engineering helps data scientists and organizations create models with the utmost accuracy and efficiency.

How does it work?

The process of feature engineering may look something like this:

Formulate Features – Study a lot of data, examine feature engineering on multiple problems, and discover what to utilize from them.

Explain Features – It involves two procedures: feature extraction and feature construction. Users can decide to utilize manual feature construction, automatic feature extraction, or a combination of both, depending on the problem.

Select Features – After defining the potential features, the next step involves choosing the right features. It is comprised of two elements: feature selection and feature scoring.

Evaluate Models – It involves evaluating features by assessing the model’s accuracy on unseen data.