Outlier Detection Techniques to Know for Statistical Prediction

Outlier detection techniques are essential for improving statistical predictions. By identifying unusual data points, methods like Z-score, IQR, and Isolation Forest help ensure that models are accurate and reliable, leading to better insights and decisions.

1. Z-score method

   • Measures how many standard deviations a data point is from the mean.
   • A Z-score above 3 or below -3 is often considered an outlier.
   • Assumes a normal distribution of the data, which may not always be the case.

2. Interquartile Range (IQR) method

   • Calculates the range between the first (Q1) and third quartiles (Q3) of the data.
   • Outliers are defined as points below Q1 - 1.5IQR or above Q3 + 1.5IQR.
   • Robust to non-normal distributions and skewed data.

3. Local Outlier Factor (LOF)

   • Evaluates the local density of data points to identify outliers.
   • Compares the density of a point to that of its neighbors.
   • Effective for detecting outliers in clusters and varying density.

4. Isolation Forest

   • Constructs a forest of random trees to isolate observations.
   • Outliers are identified as points that require fewer splits to isolate.
   • Works well with high-dimensional data and is efficient in computation.

5. DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

   • Groups together points that are closely packed and marks points in low-density regions as outliers.
   • Requires two parameters: epsilon (neighborhood radius) and minPts (minimum points to form a cluster).
   • Effective for discovering clusters of varying shapes and sizes.

6. Mahalanobis Distance

   • Measures the distance of a point from the mean of a distribution, accounting for correlations between variables.
   • Useful for multivariate outlier detection.
   • A high Mahalanobis distance indicates a potential outlier.

7. One-class SVM

   • A variation of Support Vector Machines designed for outlier detection.
   • Trains on normal data to create a boundary that separates normal points from outliers.
   • Effective in high-dimensional spaces and when labeled data is scarce.

8. Elliptic Envelope

   • Fits an ellipse around the central data points to identify outliers.
   • Assumes a Gaussian distribution of the data.
   • Provides a probabilistic approach to outlier detection.

9. Cook's Distance

   • Measures the influence of each data point on the overall regression model.
   • Points with a Cook's Distance greater than 1 are considered influential and potential outliers.
   • Useful in regression analysis to assess the impact of outliers.

10. Robust Random Cut Forest

    • An ensemble method that uses random cuts to partition data and identify anomalies.
    • Robust to noise and can handle high-dimensional data.
    • Provides a probabilistic score for each point indicating its likelihood of being an outlier.