Quantile Discretizer Transform

QuantileDiscretizer takes a column with continuous features and outputs a column with binned categorical features.

Input

It takes in a DataFrame and transforms it to another DataFrame

Output

The output DataFrame contains a new column of binned categorical features.

Type

ml-predict

Class

fire.nodes.ml.NodeQuantileDiscretizerTransform

Fields

Details

Quantile Discretizer Transform Node Details

The Quantile Discretizer Transform Node is used to discretize a continuous feature into a categorical feature by specifying a set of boundaries. It takes in an input DataFrame and transforms it to another DataFrame. It also takes in a fit model as input, which is typically the output of a previous Quantile Discretizer Estimator Node.

The transformed DataFrame contains a new column with the binned categorical features.

Input Parameters

FIT MODEL : The output of a previous Quantile Discretizer Estimator Node, which contains the specified boundaries for the discretization.

Examples

Quantile Discretizer Transform Node Example

Consider the following example, where we have a DataFrame with a column ‘age’ containing continuous values. We use a Quantile Discretizer Estimator Node to specify the boundaries for the discretization, creating a fit model. Then, we use the Quantile Discretizer Transform Node to discretize the ‘age’ column using the fit model.

Input DataFrame:

id age

1 22

2 35

3 45

4 55

Output

id age age_binned

1 22 1

2 35 2

3 45 3

4 55 4

In this example, the input column is ‘age’ and the output column is ‘age_binned’. The fit model is created using the Quantile Discretizer Estimator Node. The Quantile Discretizer Transform Node uses this fit model to discretize the ‘age’ column, creating a new column ‘age_binned’ with the binned categorical features.