Show Menu
TOPICS×

10.4 Experimentation - Recipe Building

What is a Recipe?

A recipe is a proprietary algorithm or an ensemble of algorithms to help solve a specific business problem.
The feature engineering and model that you have prepared in the previous exercises can be converted into a recipe which can be used to train multiple datasets by changing a set of hyper-parameters, like the learning rate, estimators, etc. to evaluate which training run gives the maximum accuracy and recall.
Open the notebook Mod10 - Recipe Builder-Churn Recipe.ipynb .
Notice that the recipe has all the feature engineering code used to build the model from previous exercises.
The recipe has three parts for execution—Train, Score and Create Recipe. You should be able to view these options once you open the above code.

Train the model

Click on Train - This will take a few minutes to complete. Once you see TRAINING SUCCESSFUL! in your file, you can move to the next step.

Score the model

Click on Score - Wait until you see scoring successful and you see an output of the data with the scores. Once you see SCORING SUCCESSFUL! in your file, you can move to the next step.

Create a recipe from the model

When you are satisfied with the outputs of training and scoring, you can create a recipe. Click the Create Recipe button to start the process.
Creating a recipe enables you to test your model at scale.
After clicking the Create Recipe button, you have to enter a name for your recipe.
As a naming convention, please use:
  • ldap ChurnPrediction
Replace ldap with your ldap.
Example: for ldap vangeluw , the name of your recipe should be: vangeluwChurnPrediction .
After entering a Recipe Name, click OK .
A second popup is shown, telling you that your Recipe is being created. This could take up to 5 minutes, please wait until the process finishes.
For now, click the Dismiss button.
You can view the progress of the recipe creation process in the top right corner of Jupyter Notebooks.
...
After a couple of minutes, the recipe creation is finished and you can find your recipe in the Recipes section. To go there, click on Recipes in the ML Models menu.
You'll find your recipe in the list.
From this list, your Recipe can be used to experiment but no coding is required beyond this stage anymore.