{
  "slug": "z-score-normalize",
  "name": "gizza-ai/z-score-normalize",
  "version": "0.1.0",
  "title": "Z-Score Normalize — Z-Score, Min-Max, Max-Abs & Robust Scaling — gizza.ai",
  "description": "Standardize a list of numbers with z-score, min-max, max-abs, or robust median/IQR scaling. Population or sample standard deviation, private and browser-local.",
  "tags": [
    "z-score",
    "z-score normalization",
    "standardize",
    "standardization",
    "standard score",
    "min-max scaling",
    "max-abs scaling",
    "robust scaling",
    "median",
    "interquartile range",
    "feature scaling",
    "normalize numbers",
    "standard deviation",
    "rescale",
    "statistics",
    "machine learning"
  ],
  "category": "math",
  "urls": {
    "page": "https://gizza.ai/tools/z-score-normalize/",
    "markdown": "https://gizza.ai/tools/z-score-normalize/index.md",
    "descriptor": "https://gizza.ai/tools/z-score-normalize/tool.json",
    "deep_link_example": "https://gizza.ai/tools/z-score-normalize/?numbers=2%2C%204%2C%204%2C%204%2C%205%2C%205%2C%207%2C%209&method=z-score&sample=true"
  },
  "cli": "gizza tool z-score-normalize \"2, 4, 4, 4, 5, 5, 7, 9\"",
  "tool": {
    "description": "Normalize a list of numbers (separated by spaces, commas, semicolons, or newlines). method='z-score' (default) standardizes each value to mean 0 and standard deviation 1 by subtracting the mean and dividing by the standard deviation (population ÷N by default, or sample ÷N−1 when sample=true); method='min-max' linearly rescales values into the 0–1 range; method='max-abs' divides by the largest absolute value, mapping into [−1, 1] while preserving sign; method='robust' subtracts the median and divides by the interquartile range (Q3−Q1) to resist outliers. Returns the transformed values plus the parameters used (mean/std dev, min/max, max abs, or median/IQR). Runs locally.",
    "parameters": {
      "additionalProperties": false,
      "properties": {
        "method": {
          "default": "z-score",
          "description": "'z-score' (default) standardizes to mean 0 and standard deviation 1; 'min-max' rescales linearly into the 0–1 range; 'max-abs' divides by the largest absolute value, mapping into [−1, 1] while preserving sign; 'robust' subtracts the median and divides by the interquartile range (Q3−Q1), resisting outliers.",
          "enum": [
            "z-score",
            "min-max",
            "max-abs",
            "robust"
          ],
          "type": "string"
        },
        "numbers": {
          "description": "The numbers to normalize, separated by spaces, commas, semicolons, or newlines.",
          "type": "string"
        },
        "sample": {
          "default": false,
          "description": "z-score only: when true use the sample standard deviation (÷N−1); default false uses the population standard deviation (÷N), matching scikit-learn StandardScaler. Ignored for the other methods.",
          "type": "boolean"
        }
      },
      "required": [
        "numbers"
      ],
      "type": "object"
    }
  }
}