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Create a recipe (optional)

Create a data-wrangling recipe to transform your dataset. When the recipe is complete, it will be applied to the input dataset to create a new transformed dataset.

Tip: You may skip this step and create an app directly after importing your dataset if your dataset is already ready for machine learning and do not require any transformation.

1. Create a recipe

Continuing from the previous article, Create a new dataset, select "Create a new data wrangling recipe", name your recipe, and click "Create".

Tip: Alternatively, you can select "Create a recipe" from the dataset listing page or the dataset detail page.

Note: For more information, see What is a recipe?

Create a Recipe

2. Start a recipe-building session

Immediately, you will enter the recipe-building session. The Engine will need 1-2 minutes to prepare the session before you can begin.

3. Add suggestions

The Engine will automatically generate suggestions on what actions to add to the recipe. These suggestions are shown in the suggestions tab.

Click on the (+) buttons next to all 3 suggestions to queue them up in the recipe:

  • Convert columns to categorical type
  • Convert columns to numeric type
  • Drop columns

After you are done adding all 3 suggestions, click "commit action" in the recipe panel.

Tip: Are you curious about why the Engine provided these suggestions? Click on "see analysis" in the suggestion box to find out.

Note: For more information, see What are suggestions?

4. Add actions

Next, we want to add a specific action/s to the recipe:

  • Drop columns: Column1 is just the row number

Add Drop Columns

  1. Click on the "Add Actions" tab.
  2. In the search field, enter "Drop".
  3. Select the Drop action
  4. Under Input Columns, select "Column1"
  5. Click ADD to add the action to the queue

Note: To see a full list of actions supported in the Engine, see "What actions are available in the Engine?".

5. Commit actions

Select the "RECIPE" tab, and at the bottom of tab, click on "Commit Actions".

Caution: Once actions are committed, they can no longer be edited.

This will apply the actions onto the full dataset and generate a fresh set of suggestions based on the latest dataset. At this stage, you may choose to repeat steps (3) and (4) to further transform the dataset as desired.

For this tutorial, we are happy with the current state of the dataset. Proceed to the next step to finalize and end.

6. Finalize & end

Click on "Finalize & end" to finalize the recipe. This will generate a transformed dataset (german_credit_score - Processed) by applying the actions in your recipe to the selected input dataset.

At the time of finalizing the recipe, any queued action will automatically be committed.

Caution: Finalized recipes are no longer editable.