After creating a recipe, you can now train and score your recipe without having to touch code again.
Login to Adobe Experience Platform: https://experience.adobe.com/platform
From the left menu, click on Models.
In this exercise, we’ll use a prebuilt recipe to create a Model for Car Insurance Sales Predictions.
In the top menu, click on Recipes.
In Recipes, you’ll find multiple recipes. Look for your own recipe in the list, which should be named
ldapCarInsurancePropensity recipe to open it.
You now need to create your own Model, based on the
To do that, click on the Create a Model button.
To train this model, you need to provide it with an Input Dataset. In our case, the dataset to use is called
AEP Demo - Car Insurance Interactions. Select it from the list.
Click Next to continue.
In the next step, you need to define a name for your Model. As a naming convention, let’s use:
ldap - CarInsurancePropensity Model and replace
ldap with your ldap.
Example: for ldap vangeluw, the name become
vangeluw - CarInsurancePropensity Model.
We can also hyper-tune the Model by changing the Model Configuration. To do that, you can for instance change the n_estimators or the max_depth.
If you want to update the Model’s Configuration parameters, double-click one of the parameters and give it a new value.
Next, click Finish to finish your configuration.
After a couple of seconds, you’ll be reverted back to the Model’s homepage where you’ll see a Training Run 1 with a status of Pending. The process to finish the training run can take 5+ minutes.
After 1-2 minutes, your Training Run’s status will change to Running.
And 1-2 minutes later, the Training Run’s status will change to Complete.
Once the Training Run has completed, you’ll see an Accuracy Metric that indicates how successful the model is. (Due to the lack of training data, accuracy doesn’t say much in this demo situation).
Training a model requires more then one run. All Training Runs will be visible on this page and you’ll be able to compare their results, so you can decide which one is the most successful.
After training a model, we can use the model to score and as such, have the model calculate Car Insurance Sales Propensity scores which can be activated through targeting.
To start scoring, let’s re-open Training Run 1 by clicking it.
After opening Training Run 1, you’ll see a full overview of the Training Run, and in the future, more visualization options will be added.
To score, you have to click the + Score button in the top right corner of your screen.
In the next step, you again have to select an Input Dataset. Let’s choose the
AEP Demo - Car Insurance Interactions dataset.
After selecting the Input Dataset, click Next.
In the next step, you need to select a dataset to which Platform will output results. In this case, select the
AEP Demo - ML Predictions dataset.
After selecting the Output Dataset, click Next.
In the next screen, you can again specify/change some of the Model’s Configuration parameters.
After updating the Model’s Configuration parameters, click Finish.
A Scoring Run is now created, and has a status of Running.
And 1-2 minutes later, the Scoring Run’s status will change to Complete.
And finally, let’s preview the results. Click on Scoring Run 1
Next, click the Preview Scoring Results Dataset.