Feature Selection
The best method to choose the most vital features and discard the rest
We might have a bunch of data. But are they all valuable and relevant? Which columns and characteristics are most likely to have an impact on the outcome?
Here, I will explain this with the help of an example. By the end of this article, you will be able to identify the important features to keep and discard the rest.
Some of our data is frequently irrelevant to our analysis. For example:
- does the startup’s name influence its fundraising success?
- Is there a link between a person’s preferred color and intelligence?
Selecting the most relevant characteristics is also an important challenge in data processing. Why would we waste valuable time and computational resources by considering unnecessary features/columns in our analysis? Worse, would the irrelevant attributes distort our analysis? DEFINITELY YES.
For example, we may have 20 or more attributes that characterize our consumers. These characteristics include age, salary range, location, gender, whether or not they have children, spending level, recent purchases, greatest educational attainment, whether or not they own a home, and a slew of others. However, not all of these are likely to be relevant to our study or predictive model. Although it is feasible that all of these factors will have an influence, the analysis may be too complicated to be meaningful.
Feature Selection is a method of simplifying analysis by emphasizing relevance. But how can we tell if a certain trait is important? Here’s when domain knowledge and experience come into play.
For example, the data analyst or team should be familiar with retail (in our example above). As a result, the team will be able to carefully choose the elements that will have the most influence on the prediction model or analysis.
Here are a few tips to keep the important features:
- Begin by asking the correct questions before focusing on applying the most complex algorithm to the data.
- To choose the proper (and most relevant) questions, you or someone on your team should be knowledgeable about the issue.
- Need to have the domain knowledge to know which features are interdependent with one another.
- Required to analyze the business data. E.g, more customers also means more sales. People from higher income groups might also have higher spending levels.
- Professionals frequently experiment with different combinations to find which produces the greatest outcomes (or look for something that makes the most sense).
Key Takeaway
Choosing the best characteristics may also take some time, especially if you’re working with a large dataset (with hundreds or even thousands of columns). This may take some time to identify important features while working on a huge dataset but, with practice, you will have a grip on this.
In general, subject knowledge may be more valuable than data analytic skills.
Thank you for reading! I would appreciate it if you follow me or share this article with someone. Best wishes.
