infer_gender

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

Example 1
Example 2
Signature

To use the default labels “male” and “female” in the resulting output simply use

infer_gender(ds.first_name) -> (ds.gender)

To use labels “M” and “F” instead

infer_gender(ds.first_name), "labels": {"male": "M", "female": "F"}) -> (ds.gender)

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.

infer_gender(first_name: category, {
    "param": value,
    ...
}) -> (gender: sex)

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

column[category]

required

Column containing first names.

Outputs

column[sex]

required

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).

Parameters

object

Labels for the male and female categories. An object mapping the “male” and “female” categories to custom labels.

Properties

string

default:"male"

Label for the “male” category.

string

default:"female"

Label for the “female” category.

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Usage

Inputs & Outputs

Configuration

​Usage

​Inputs & Outputs

​Configuration

Usage

Inputs & Outputs

Configuration