Feature engineering is a major part of building a machine learning model, but it doesn’t get the attention that other aspects typically get. Creating features is a very difficult process, and you need to do a lot of things well to do it correctly. The big issue is that the current tools and methodologies don’t always provide everything you need to do it correctly.
Feature engineering involves learning and understanding parameters that need to be transformed within a machine learning model. In practice, it means spending a lot of time figuring out what parameters are most important when building a model. When you start to figure that out, you get closer to building out features that make a lot of sense for your specific needs. The main tool for doing it is in the feature module of Sciki-learn. This module has some shortcomings, but there are things that can help you get even better results.
Common Ways We Do Feature Engineering
As mentioned above, feature engineering is a crucial component of building a machine learning model. Creating features has become a science and art within itself, and it is something that every machine learning engineer needs to master. The most difficult thing is that feature engineering is quite tedious and time-consuming. If you don’t do it well, you also end up completely destroying whatever good results you could hope for.
The major innovation in feature engineering is automating a significant portion of the process for creating features. This is where having a feature engine that can automatically recognize certain numerical data points that can be turned into features comes into play. An amazing tool like this is critical when building features for your machine learning model.
Why Feature Engineering Is Hard
The big problem with feature engineering is that it is time-consuming and tedious. Feature engineering is the process of figuring out what data matters when building a machine learning model. You have to analyze each available and see how it relates back to the overall population that you are modeling. Because of that, it can be a difficult process if you don’t intuitively understand what is going on. You also go through plenty of repetitive data processing steps, making the process even tricky and more drawn out as you go on.
After all of that, you still need to understand the features after transforming them into something else. Your model still needs to be understood by outside parties, and this might not be possible after you have performed certain feature engineering steps on it. A feature engine minimizes many of these problems, but it won’t eliminate them altogether.
Simplifying the Process
The big thing that a feature engine does is to lessen the time you spend processing data and transforming it. In practice, it means that you spend more time looking at a big picture view of your machine learning project than going into the details to do feature engineering on a granular scale. It gives you a much better idea of what is going on, which can be a crucial component of success when working with feature engineering.