💡So, What’s A Feature?
A feature is a individual measurable property or a characteristic of a data point that is input for a machine learning model. Some characteristics of a feature function in order to describe the kind of data that you’re working with. This means that characteristics of a feature includes being able to make it numerical, categorical, or text-based. For example, we could be working with a housing price dataset. What can be classified as features in this dataset could be the number of bedrooms a house has, a house’s square footage, a house’s location, or the age of the property. The quality of the features, as well as your choices of features, are essential in machine learning. Features impact the overall accuracy and performance of a machine learning model.
💡Now, What’s Feature Engineering?
Feature Engineering is the process of making new features, or transforming existing features. The high-level overview of feature engineering involves turning raw data into a format that the machine can understand. Activities done within feature engineering include selecting, extracting, and transforming the most relevant features from data in order to build more accurate models. Many techniques exist for creating new features, through either combining or transforming existing ones. All of these techniques exist because they help to highlight the most important patterns and relationships in the data. This in turn helps the machine learning model to learn from the data more efficiently.
💡Why Is It Involved in Machine Learning?
The goal of feature engineering is to improve model accuracy by providing more meaningful and relevant information. A model is considered to be successful depending on the quality of its features, which are used to train the model itself. There are also five other big reasons for why feature engineering is needed within machine learning, which include improving user experience, gaining competitive advantage, meeting customer needs, increasing revenue, and future-proofing businesses.
To improve a user’s experience, machine learning takes data from products and services in order to make them more intuitive, user-friendly, and fulfilling. For general business, machine learning can give businesses a competitive advantage by offering new insights on how to differentiate their product from competitors. For meeting customer needs, you can engineer features that reflect your market analyses and insight gained from user feedback. Next, increases in revenue can be achieved when features are engineered to give more functionality to lead more upsells. Lastly, future-proofing could be an exceptional benefit when you engineer features to anticipate future trends through potential customer needs, by ensuring that the product stays relevant.
Congrats 🎉, you’ve gained some clarity on what feature engineering is and why it’s relevant within machine learning! 🙌 Keep your eyes out for Part 2, where I’ll cover processes within feature engineering at a high-level overview.