> For the complete documentation index, see [llms.txt](https://docs.aporia.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.aporia.com/model-types/binary.md).

# Binary Classification

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:

<table><thead><tr><th width="76">id</th><th width="132">feature1 (numeric)</th><th width="141">feature2 (boolean)</th><th width="122">decision (boolean)</th><th>label (boolean)</th><th width="191">timestamp (datetime)</th></tr></thead><tbody><tr><td>1</td><td>13.5</td><td>True</td><td>True</td><td>True</td><td>2014-10-19 10:23:54</td></tr><tr><td>2</td><td>-8</td><td>False</td><td>False</td><td>True</td><td>2014-10-19 10:24:24</td></tr></tbody></table>

To integrate this type of model follow our [Quickstart](/introduction/quickstart.md), 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](/data-sources/overview.md) 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:

<table><thead><tr><th width="82">id</th><th width="116">feature1 (numeric)</th><th width="112">feature2 (boolean)</th><th width="112">proba (numeric)</th><th width="138">decision (boolean)</th><th width="122">label (boolean)</th><th width="196">timestamp (datetime)</th></tr></thead><tbody><tr><td>1</td><td>13.5</td><td>True</td><td>0.8</td><td>True</td><td>True</td><td>2014-10-19 10:23:54</td></tr><tr><td>2</td><td>-8</td><td>False</td><td>0.5</td><td>False</td><td>True</td><td>2014-10-19 10:24:24</td></tr></tbody></table>

To integrate this type of model follow our [Quickstart](/introduction/quickstart.md), 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](/data-sources/overview.md) for more information about how to connect from different data sources.

### 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.&#x20;


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