New in the Spring ’20 Release: Filter-Based Predictions!
by Sara Asher, Senior Director of Product Management at Salesforce
Hi everyone! I’m very excited about one of our new features in the Spring ’20 release:“Filter-based predictions.” The purpose of this feature is to make it easy to construct your prediction within the Einstein Prediction Builder (EPB) wizard without needing to have a field to predict prepared in advance.
Before we go into details about filter-based predictions, let’s talk a little bit about how predictions work in general. Einstein Prediction Builder (and other machine learning systems) learn from examples in the past to make predictions on the future. One of the types of problems Einstein Prediction Builder concentrates on are yes/no questions (binary classifications). Examples of yes/no questions are: Is this opportunity going to convert? Is this invoice going to be on time? Is this student going to graduate high school? For new records, Prediction Builder will return a number between 0 and 100 that represents the likelihood that the answer to the question is yes.
In order for Einstein Prediction Builder to try to answer a yes/no question, it needs to have example records where the answer is definitely yes (positive examples), and example records where the answer is definitely no (negative examples). Once Einstein Prediction Builder has those examples, it can then predict on new records or on records where we don’t know the answer to the question yet.
So how do you tell EPB which records are your positive examples, and which records are your negative examples? In previous releases of Einstein Prediction Builder, you could select a checkbox and define an example set. Then, all records in the example set that were checked are the positive examples, and all records in the example set that were unchecked were the negative examples. (And the everything not in the example set would get predictions.)
That system worked pretty well for many use cases, but what should you do if you don’t have a checkbox like that? There is the option of creating a formula field, but that tends to be complicated and perhaps a bit error-prone. Instead, there should be an easy way for you to simply tell us which are the positive examples and which are the negative examples.
Introducing filter-based predictions!
Now, instead of needing an already existing field that represents what you want to predict, you can directly define your positive and negative examples within the wizard. Let’s run through an example to see how it works.
Let’s jump back to the question of whether a bill is going to be paid on-time. The positive examples are invoices where that balance is zero and was paid before the due date, and the negative examples (the late invoices) are ones where they paid, but the invoice was only paid off after the due date OR they still have a balance due, and the due date has already passed.
It is pretty easy to think of these as filters on your data.
Positive examples: Records where invoice balance is 0, and the last payment date was before the due date
Negative examples: Payment date is after due date OR it’s past due date, and there is still a balance
One note to consider when setting up filters like this: your “yes” examples and your “no” examples should be distinct from each other. Einstein can’t differentiate between your positive and negative examples if your records are both at the same time!
Anyway, now that we have a feel for how we could create filters to represent our positive and negative examples, let’s see how we would create this prediction within Einstein Prediction Builder.
Building a filter-based prediction
Just like any prediction, you first need to tell us what object the prediction is for and then let us know what kind of prediction you intend to create. In this case, the prediction is on the Invoice object, and we wish to answer a Yes/No question.
At this point, you will notice a new question from Einstein Prediction Builder: Is there a field that can answer your prediction question? If you already have a checkbox or formula field set up, choose the “Field” option. But in this case, we don’t have a field, so we choose the “No Field” option.
After selecting that option, you will see that you now have the opportunity to fill in “Yes” examples and “No” examples, which we can do just as we planned out above:
Once you’ve entered in your two filters, you can validate your logic by using the data checker to the right of the screen. The data checker will tell you how many positive and negative examples you have and how many records will receive predictions. The rest of the wizard remains the same. And that’s it! You can now create predictions without needing to have a field to predict prepared in advance.
We hope these new filter-based predictions are helpful when setting up your new predictions! Please try it out, let us know if you like it, and have fun predicting!
