statistical_outlier_detector
OfficialDetect and manage outliers in structured data, improve data integrity.
Data & Analytics#data quality#data cleaning#statistical analysis#Z-score#IQR#outlier detection#IsolationForest
Authorcas-bigdatalab
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill efficiently identifies and handles outliers in structured data, ensuring data integrity and improving analytical accuracy.
Core Features & Use Cases
- Outlier Detection: Identifies and flags data points that deviate significantly from the expected distribution.
- Data Transformation: Offers actions like marking, removing, or clipping outliers.
- Scalable Methods: Utilizes IQR, Z-score, IsolationForest, and Group Standard Deviation for varied scenarios.
- Use Case: In quality control or experimental analysis, it helps detect anomalies that might affect the conclusions or process decisions.
Quick Start
Use the statistical_outlier_detector skill with --input mydata.csv --output processeddata.csv --method iqr --action mark to identify and mark outliers in a dataset.
Dependency Matrix
Required Modules
pandasscipyscikit-learnnumpy
Components
scripts
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: statistical_outlier_detector Download link: https://github.com/cas-bigdatalab/piflow/archive/main.zip#statistical-outlier-detector Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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