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Binary classification models predict a binary outcome (one of two possible classes). In Aporia, these models are represented by the binary model type.
Examples of binary classification problems:
- Will the customer
buythis product or
- Is this email
- Is this review written by a
Frequently, binary models output not only a yes/no answer, but also a probability.
If you have a model with a yes/no decision but without a probability value, then your database may look like the following:
If you have a model with a yes/no decision and a probability / confidence value for it, then your database may look like the following:
To integrate this type of model follow our Quickstart, and during the schema mapping remember to include a
booleanprediction, a proba
booleanactual field and linked them together. In case you want to link both the proba and the boolean prediction to the actual field, just add a duplicate of the actual column in the query defining the dataset, so you'll have 2 actual fields in your schema and link each of them to one of the prediction fields.
In cases when there is no threshold for your boolean prediction, and the final business result is actually a probability, you may simply omit the
decisionfield from the examples in the previous section and only include the
probafield for your prediction.