discretize_on_values
Discretize column by binning its values using explicitly specified cuts points.
Each bin can optionally be assigned a label.
Usage
The following example shows how the step can be used in a recipe.
Examples
Examples
The following parameters will discretize a column with values in the range [0, 1], producing a new categorical column with bins (0, 0.33], (0.33, 0.66], (0.66, 1] (i.e. right-inclusive). The bins will be labelled “low”, “medium”, and “high” respectively.
The following parameters will discretize a column with values in the range [0, 1], producing a new categorical column with bins (0, 0.33], (0.33, 0.66], (0.66, 1] (i.e. right-inclusive). The bins will be labelled “low”, “medium”, and “high” respectively.
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
A quantitative column to discretize.
Outputs
Outputs
A new categorical column with categories corresponding to discretized bins.
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
Parameters
Points/values used to cut the quantitative column into bins.
Array items
Array items
Each item in array.
Names for the resulting bins. Important: Note that cutting a series of values in 3 places creates 4 bins..
Array items
Array items
Each item in array.
Whether to automatically include the minimum and maximum values of the column as cut points.
Whether the intervals are right-inclusive, i.e. of the form (x1, x2], or left-inclusive [x1, x2).