normalize
Normalizes a numerical column by subtracting the mean and dividing by its standard deviation.
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.
Usage
The following examples show how the step can be used in a recipe.Examples
Examples
To normalize using both mean and standard deviation:
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").
Inputs
Inputs
A numeric column to normalize.
Outputs
Outputs
A numeric column containing the normalized value.
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).