infer_gender
Try to infer a person’s gender given a first name.
Uses a machine learning model trained on a large database of names and the frequencies of associated genders.
Usage
The following examples show how the step can be used in a recipe.
Examples
Examples
To use the default labels “male” and “female” in the resulting output simply use
To use the default labels “male” and “female” in the resulting output simply use
To use labels “M” and “F” instead
General syntax for using the step in a recipe. Shows the inputs and outputs the step is expected to receive and will produce respectively. For futher details see sections below.
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
Column containing first names.
Outputs
Outputs
Predicted gender for each name.
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).