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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>
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1.0 KiB
name, kind, lang, domain, version, purity, signature, description, tags, uses_functions, uses_types, returns, returns_optional, error_type, imports, params, output, tested, tests, test_file_path, file_path
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| detect_outliers | function | py | datascience | 1.0.0 | pure | def detect_outliers(data: list, threshold: float) -> list | Detecta outliers por z-score. Retorna lista de bools, True donde |z-score| > threshold. |
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false |
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lista de booleanos paralela a data, True donde |z-score| > threshold | false | python/functions/datascience/datascience.py |
Ejemplo
detect_outliers([1, 2, 3, 100, 2, 3], 2.0)
# [False, False, False, True, False, False]
Notas
Usa z-score poblacional. Threshold tipico: 2.0 o 3.0. Si la desviacion es cero, no hay outliers.