# 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*.

Example: Boolean Decision without 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.

Example: Boolean Decision with Probability

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.

Example: Probability Only

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 |