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 --------- .. list-table:: :widths: 10 5 10 :header-rows: 1 * - Name - Title - Description 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.