OutlierDetection

Detects outliers in numerical features using multiple statistical methods.

Purpose

This test identifies outliers in numerical features using various statistical methods including IQR, Z-score, and Isolation Forest. It provides comprehensive outlier detection to help identify data quality issues and potential anomalies.

Test Mechanism

The test applies multiple outlier detection methods: - IQR method: Values beyond Q1 - 1.5IQR or Q3 + 1.5IQR - Z-score method: Values with |z-score| > threshold - Isolation Forest: ML-based anomaly detection

Signs of High Risk

• High percentage of outliers indicating data quality issues
• Inconsistent outlier detection across methods
• Extreme outliers that significantly deviate from normal patterns

Strengths

• Multiple detection methods for robust outlier identification
• Customizable thresholds for different sensitivity levels
• Clear summary of outlier patterns across features

Limitations

• Limited to numerical features only
• Some methods assume normal distributions
• Threshold selection can be subjective