Principal Component Analysis

This transform is used for reducing the number of features in a dataset, while preserving the important information. This feature engineering transform is applicable only to numerical data.

tags: [“Data Preparation”]

Parameters

The table gives a brief description about each parameter in the Principal Component Analysis transform.

Name:

By default, the transform name is populated. You can also add a custom name for the transform.

Input Dataset:

The file name of the input dataset. You can select the dataset that was uploaded from the drop-down list. (Required: True, Multiple: False)

Output Dataset:

The file name with which the output dataset is created. (Required: True, Multiple: False)

Number of Components:

The number of principal components. (Required: True, Multiple: False, Datatypes: [“LONG”], Options: [‘CONSTANT’])

Target Feature:

The target feature or column of the Dataset. (Required: True, Multiple: False, Options: [‘FIELDS’], Datasets: [‘df’])

The sample input for this transform looks as shown in the screenshot:

The output after running the Principal Component Analysis transform on the dataset appears as below:

How to use it in Notebook

The following is the code snippet you must use in the Jupyter Notebook editor to run the Principal Component Analysis transform:

template=TemplateV2.get_template_by('Principal Component Analysis')

recipe_Principal_Component_Analysis= project.addRecipe([car_data, employee_data, temperature_data, only_numeric], name='Principal Component Analysis')

transform=Transform()
transform.templateId = template.id
transform.name='Principal Component Analysis'
transform.variables = {
'input_dataset':'only_numeric',
'output_dataset':'principal_comp',
'n':1,
'target':"Age"}
recipe_Principal_Component_Analysis.add_transform(transform)
recipe_Principal_Component_Analysis.run()

Requirements

scikit-learn pandas