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The resulting column’s values will have a mean of 0.0 and a standard deviation of 1.0. Both types of scaling can be toggled separately via the with_mean and with_std parameters.

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Usage

The following examples show how the step can be used in a recipe.

Examples

  • Example 1
  • Example 2
  • Signature
To normalize using both mean and standard deviation:
normalize(ds.input) -> (ds.normalized)

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Inputs & Outputs

The following are the inputs expected by the step and the outputs it produces. These are generally columns (ds.first_name), datasets (ds or ds[["first_name", "last_name"]]) or models (referenced by name e.g. "churn-clf").
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input
column[number]
required
A numeric column to normalize.
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output
column[number]
required
A numeric column containing the normalized value.

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Configuration

The following parameters can be used to configure the behaviour of the step by including them in a json object as the last “input” to the step, i.e. step(..., {"param": "value", ...}) -> (output).

Parameters

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with_mean
boolean
default:"true"
Whether to subtract the mean.
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with_std
boolean
default:"true"
Whether to divide by the standard deviation.