Another crucial technique for identifying outliers is the Interquartile Range (IQR) method. This strategy focuses on the middle 50% of the data, housed between the 25th percentile (Q1) and the 75th percentile (Q3), to establish a range within which most data points should fall. The IQR is calculated as:
IQR=Q3−Q1
Any data point that lies beyond (Q1−1.5×IQR) or (Q3+1.5×IQR) is considered an outlier, paralleling how an extraordinarily priced property sticks out in a market analysis. The choice of 1.5 as the multiplier when applying the IQR method is not arbitrary; it is based on a balance between identifying genuine outliers and preserving as much data as possible. A multiplier of 1.5 extends beyond the middle 50% of the data to capture extreme values while minimizing the risk of labeling too many points as outliers. This convention is rooted in statistical practices, aiming to strike a practical balance—recognizing that while some data points may appear far from the central cluster, they are not so extreme as to be considered anomalies in every case. The multiplier can be adjusted in practice, with 1.5 serving as a widely accepted starting point that provides a reasonable trade-off between sensitivity and specificity in outlier detection.
Here's how we can implement the IQR method in Python using the same California Housing Dataset, specifically examining the "Median Income" column for outliers:
The IQR method allows for a nuanced evaluation of data points by considering the distribution's middle spread, thereby offering a complementary perspective to the z-score method in the identification and treatment of outliers.
