Binary Classification
Last updated
Last updated
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:
id | feature1 (numeric) | feature2 (boolean) | decision (boolean) | label (boolean) | timestamp (datetime) |
---|
To integrate this type of model follow our , and during the schema mapping remember to include a boolean
prediction field and boolean
actual field and linked them together.
Check out the 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:
id | feature1 (numeric) | feature2 (boolean) | proba (numeric) | decision (boolean) | label (boolean) | timestamp (datetime) |
---|
To integrate this type of model follow our , 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.
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.
Check out the for more information about how to connect from different data sources.
1 | 13.5 | True | True | True | 2014-10-19 10:23:54 |
2 | -8 | False | False | True | 2014-10-19 10:24:24 |
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 |