Interquartile Range (IQR) Detection

https://github.com/rhyslwells/ML_Tools/blob/main/Explorations/Preprocess/Outliers/outliers_IQR.py

Context:

The IQR method is a robust and widely used statistical technique for identifying outliers, especially in univariate data. It is based on the distribution of data and is less sensitive to extreme values compared to methods reliant on mean and standard deviation.

Steps:

• Compute the IQR:
  • The IQR is the range within which the central 50% of the data lies.
  • Formula:
    $\text{IQR}=Q3-Q1$
    where:
    • $Q1$: The first quartile (25th percentile)
    • $Q3$: The third quartile (75th percentile).
• Determine the bounds:
  • Define lower and upper bounds to detect potential outliers:
    $\text{Lower Bound}=Q1-1.5\cdot \text{IQR}$
    $\text{Upper Bound}=Q3+1.5\cdot \text{IQR}$
• Identify anomalies:
  • Any data point outside the lower or upper bounds is flagged as an anomaly.

Applications:

• Best suited for non-Gaussian distributions.
• Commonly used in boxplots for visualizing outliers.