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 buy this product or not_buy this product?
Is this email spam or not_spam?
Is this review written by a customer or a robot?
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:
1
13.5
True
True
True
2014-10-19 10:23:54
2
-8
False
False
True
2014-10-19 10:24:24
To integrate this type of model follow our Quickstart, and during the schema mapping remember to include a boolean prediction field and boolean actual field and linked them together.
Check out the data sources section for more information about how to connect from different data sources.
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:
1
13.5
True
0.8
True
True
2014-10-19 10:23:54
2
-8
False
0.5
False
True
2014-10-19 10:24:24
To integrate this type of model follow our Quickstart, and during the schema mapping remember to include a boolean prediction, a proba numeric prediction and boolean actual 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.
Check out the data sources section for more information about how to connect from different data sources.
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 decision field from the examples in the previous section and only include the proba field for your prediction.
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