Over the past few years, machine learning has slowly taken the world by storm. Whether it’s the healthcare industry or the corporate sector, you can find all kinds of machine learning models. However, the work that’s been put into these applications is nothing to scoff at. Even with a team of full-fledged data science experts, the deployment alone takes around a month.
One of the main reasons it takes so long is the complexity of data, but that’s precisely why experts have a different approach to machine learning. One particular approach that’s been gaining traction recently is feature engineering.
One of the primary purposes of machine learning (ML) models is to provide a conclusion from a data set. Usually, a predictive model consists of outcome variables and predictors. Say you want to know how many calories a person needs to consume per day. The outcome variable would be the value you wish to obtain, which would be the number of calories. On the other hand, the predictor, or ‘features,’ are values that affect the outcome variable. So, in the previous example, the values that affect the calories may include the person’s height and weight.
Feature engineering is a technique where you manipulate the existing features using domain knowledge to improve their performance. For example, you want to add more features to the dataset. One thing you can do is use the knowledge Body Mass Index (BMI). Doing so gives you one more feature, which is the BMI.
Needless to say, a model that consists of the BMI, height, and weight would perform better than a model that only consists of the latter two. In other words, feature engineering aims to improve the performance of your model by manipulating the features.
If you want to know more about this subject, you can cnvrg and other similar sites that features articles on feature engineering will help. But before anything else, why should you use feature engineering?
Issues are inevitable when developing an ML model. Your job is to minimize the frequency of these issues. One common source of such problems is missing values. If your data set has some missing values, then there are bound to be inconsistencies. Feature engineering helps eliminate these potential sources by filling in the missing values.
Typically, when you perform feature engineering to handle missing values, you need to fill in a value as close to the original as possible. You can do so by taking the average of the rest of the values and using that as the placeholder.
As you continue to develop the ML model, you’ll notice that some features have a lot of values, while others only have a few. Features with few of these values usually lead to overfitting.
Overfitting is when the model is too accurate to the point that it negatively impacts its overall performance due to misleading outcomes. One way to prevent this scenario is by combining or grouping two or more features together. However, remember that each feature has varying classes, and you can only group together features that belong to a similar class.
If feature engineering has a grouping technique, it’s only fitting if there’s only a splitting technique. Feature splitting is practically the opposite of grouping, and it’s often used to turn a single feature into two or more. Its main purpose is to help you get the data you need.
For instance, a feature is labeled ‘Name,’ and it consists of the first and last name of an individual. If you only want the last name, then you can split that feature into two.
Suppose there’s a feature called ‘Age’ in your dataset. The typical values range from two to ten, but you find out that there’s one with a value of 36. Any value that’s too far off from the rest is called an outlier, and they often negatively impact the model’s performance. If you want to eliminate the outlier’s negative effect, you can try removing it, but that would only eliminate a critical value from your dataset. Enter feature engineering.
Since feature engineering involves manipulating features and their corresponding values, you can try using this technique by changing the outlier’s value to better fit your dataset. Doing so will allow you to nullify the negative impact of outliers.
Perhaps the most significant disadvantage of using feature engineering for machine learning is that major risks come with it. To be precise, if you don’t have considerable knowledge of the domain you’re working with, then feature engineering would cause more harm than good. Hence, it’s not advisable to apply this technique blindly, but that should no longer be the case now that you know what feature engineering entails.
As usual with any technique, there are corresponding pros and cons to feature engineering. However, it should be pretty apparent that the benefits greatly outweigh the risks, so there’s no reason to pass up on this opportunity.