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75 lines
1.7 KiB
Markdown
75 lines
1.7 KiB
Markdown
# Outlier Detection
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Identify statistical outliers in numeric fields using z-score or MAD methods.
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## Overview
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Outlier detection identifies features with values that are statistically unusual compared to the dataset distribution.
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## Methods
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### Z-Score Method
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Uses mean and standard deviation:
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- Z-score = (value - mean) / standard_deviation
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- Features with |z-score| > threshold are outliers
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- Sensitive to outliers in calculation
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### MAD Method
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Uses median and median absolute deviation:
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- Modified z-score = 0.6745 * (value - median) / MAD
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- Features with |modified z-score| > threshold are outliers
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- More robust to outliers in calculation
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## Inputs
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- **Dataset**: Any dataset with numeric field
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- **Value Field**: Numeric field to analyze
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- **Method**: "zscore" or "mad" (default: "zscore")
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- **Threshold**: Z-score threshold or MAD multiplier (default: 2.0)
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## Outputs
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New dataset containing:
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- Original features
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- **Outlier Score**: Z-score or MAD score
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- **Is Outlier**: Boolean flag
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- Original attributes
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## Example
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```json
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{
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"dataset_id": 123,
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"value_field": "income",
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"method": "zscore",
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"threshold": 2.0
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}
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```
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## Background Jobs
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This analysis runs as a background job. See [Outlier Analysis Worker](../workers/outlier_analysis.md) for details.
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## Use Cases
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- Data quality assessment
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- Anomaly detection
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- Error identification
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- Extreme value analysis
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## Notes
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- Null values are excluded from calculations
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- Threshold of 2.0 identifies ~5% of data as outliers (normal distribution)
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- MAD method recommended for skewed distributions
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- Consider spatial context when interpreting results
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## Related Documentation
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- [Outlier Analysis Worker](../workers/outlier_analysis.md)
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- [Analysis API](../api/analysis.md)
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