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- .claude/CLAUDE.md - .claude/commands/subagentes.md - .claude/rules/INDEX.md - .mcp.json - bash/functions/cybersecurity/analyze_dns.md - bash/functions/cybersecurity/audit_http_headers.md - bash/functions/cybersecurity/audit_ssh_config.md - bash/functions/cybersecurity/check_firewall.md - bash/functions/cybersecurity/detect_suspicious_users.md - bash/functions/cybersecurity/encrypt_file.md - ... Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
39 lines
1.0 KiB
Markdown
39 lines
1.0 KiB
Markdown
---
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name: detect_outliers
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kind: function
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lang: py
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domain: datascience
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version: "1.0.0"
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purity: pure
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signature: "def detect_outliers(data: list, threshold: float) -> list"
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description: "Detecta outliers por z-score. Retorna lista de bools, True donde |z-score| > threshold."
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tags: [statistics, outliers, python, pendiente-usar]
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uses_functions: []
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uses_types: []
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returns: []
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returns_optional: false
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error_type: ""
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imports: [math]
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params:
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- name: data
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desc: "lista de valores numericos para detectar outliers"
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- name: threshold
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desc: "umbral de z-score absoluto (tipico: 2.0 para 95% confianza, 3.0 para 99%). Mayor = menos sensible."
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output: "lista de booleanos paralela a data, True donde |z-score| > threshold"
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tested: false
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tests: []
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test_file_path: ""
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file_path: "python/functions/datascience/datascience.py"
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---
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## Ejemplo
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```python
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detect_outliers([1, 2, 3, 100, 2, 3], 2.0)
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# [False, False, False, True, False, False]
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```
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## Notas
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Usa z-score poblacional. Threshold tipico: 2.0 o 3.0. Si la desviacion es cero, no hay outliers.
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