Ever noticed something in your data that just feels... off? Like a customer suddenly spending ₹1.5 lakh five times in one day? That’s not just odd—it’s an outlier. And in machine learning, detecting these anomalies is essential for building smarter, more accurate models.
An outlier is a data point that strays far from the usual trend. While some are just noise or errors, others could signal fraud, system issues, or even rare opportunities. Ignoring them can skew results, impact decision-making, and hide valuable insights.
Outlier detection methods vary based on the type and size of your data:
Statistical techniques like Z-Score and IQR are great for simple distributions.
Distance-based approaches (KNN, DBSCAN) spot anomalies by measuring how far points are from their neighbors.
Machine learning models like Isolation Forest and One-Class SVM handle more complex or high-dimensional data.
When working with real-world data, it's not enough to check variables one by one. Sometimes, it’s the combination of features—like time, device, and purchase type—that reveal the real anomalies. For that, techniques like PCA, Mahalanobis Distance, or autoencoders are your go-to tools.
How you handle outliers—remove, transform, or flag them—depends on your business goal. In fraud detection, for instance, they’re crucial signals.
Want to dig deeper and learn by doing? Check out Zenoffi’s hands-on data science programs. Because in machine learning, sometimes the most valuable insight is the one that doesn’t follow the rules.
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