Imagine this: you’ve just received a dataset, and your model detects some outliers. Your first thought might be, “Boom, now I’m in trouble.” You’re working on a regression model, and following the classic approach, you decide to handle these outliers because they can introduce noise, lead to large errors, and negatively impact performance.
The other day, while chatting with my spouse about house prices, I had an “AHA” moment. I want to share this idea with you and maybe challenge your perspective a little.
You see, when we encounter outliers in our dataset, what do we typically do? “Oh no, these values will mess up the model; let’s clean them up immediately!” That’s because we believe they cause noise and harm performance.
Let me give you an example: Imagine you’re building a predictive model for house prices. You find an old house in a neighborhood with a price significantly higher than expected. Your first reaction? “Ah, this must be an outlier, right?”
But wait a minute… What if this house is the only one in that area with antique features and unique characteristics? This is where I think we need to shift our perspective on outliers.
Sometimes, outliers might actually be telling us something valuable. How, you ask? Imagine spotting an unexpected dip in your sales data. It might indicate an issue with your product or the entry of a new competitor in the market. Banks have already mastered this concept—this is exactly why fraud detection departments exist!
Just like in our antique house example, these outliers could be pointing to rare events. Sudden price changes in financial data might signal significant economic events.
Moreover, seemingly “weird” customer behaviors could point to an entirely new customer segment you hadn’t noticed before. Imagine discovering a brand-new market opportunity thanks to these data points!
We need to move away from outdated thinking and adopt a more inquisitive mindset. Outliers could spark innovative ideas and challenge our assumptions.
So, what do you say? Ready to be a bit of an “outlier” yourself?
