Train Test Split

This transform allows you to split the dataset into training and testing sets to build a predictive model. You can specify the percentage of data to be used for training and testing the model.

Parameters

This table provides a brief description about each parameter in Train Test Split 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 on which train and test split transform must be applied. You can select the dataset that was uploaded from the drop-down list. (Required: True, Multiple: False)

Target column:

The target column used for predictions.

Test size:

The percentage of data to be used for testing the model. Based on this, the data will be split into two; one for testing and the other for training.

Output Train Dataset:

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

Output Test Dataset:

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

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

../../../_images/testtrain_input.png

The output after running the Train Test Split transform on the dataset appears as below. This is the output after training the model.

../../../_images/train_output.png

This is the output generated after testing the model.

../../../_images/test_output.png

How to use it in Notebook

The following is the code snippet you must use in the Jupyter Notebook editor to run the Train Test Split transform:

train_ds_name = dataset_input_name + "_train"
test_ds_name = dataset_input_name + "_test"
transform = Transform()
transform.name = "train test split"
transform.templateId = train_test_split.id
transform.variables = {
    "inputDataset": dataset_w_bin_cols.name,
    "targetCol": targetCol,
    "test_size": 0.2,
    "output_train": train_ds_name,
    "output_test": test_ds_name
}
recipe_split = project.addRecipe([dataset_w_bin_cols], name="train test split")
# recipe_split.prepareForLocal(transform, contextId="recipe_split")
recipe_split.addTransform(transform)
recipe_split.run()

Requirements

pandas