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Data science recipe steps

PDFRSS

Use these recipe steps to tabulate and summarize data from different perspectives, or to perform advanced transformations.

Topics

• BINARIZATION
• BUCKETIZATION
• CATEGORICAL_MAPPING
• ONE_HOT_ENCODING
• SCALE
• SKEWNESS
• TOKENIZATION

SCALE

Scales or normalizes the range of data in a numeric column.

Parameters

• sourceColumn – The name of an existing column.

• strategy – The operation to be applied to the column values:

  • MIN_MAX – Rescales the values into a range of [0,1]

  • SCALE_BETWEEN – Rescales the values into a range of 2 specified values.

  • MEAN_NORMALIZATION – Rescales the data to have a mean (μ) of 0 and standard deviation (σ) of 1 within a range of [-1, 1]

  • Z_SCORE – Linearly scale data values to have a mean (μ) of 0 and standard deviation (σ) of 1. Best for handling outliers.

• targetColumn – The name of a column to contain the results.

Example

{
    "Action": {
        "Operation": "NORMALIZATION",
        "Parameters": {
            "sourceColumn": "all_votes",
            "strategy": "MIN_MAX",
            "targetColumn": "all_votes_normalized"
        }
    }
}