# Binary classification evaluator

One of the default [evaluation libraries](../model-evaluation-automatic/index.md#configure-evaluation-libraries) in a modeling objective is the *binary classification* evaluator. This library provides a core set of commonly used metrics for evaluating binary classification models.

![Complete evaluation dashboard for binary classifier](../../../assets/749396cd7290e394d9a28bc9638fa5f8692cca81ac8fa652fe6907874675ec7d.webp)

## Included metrics

:::callout{theme="neutral"}
The below metrics are produced for every [subset bucket configured in the evaluation dashboard](../model-evaluation-automatic/index.md#configure-evaluation-subsets). It may not be possible to generate all metrics on every subset if, for example, the generated subset does not contain both classes in the model prediction or label columns.
:::

The default binary classification evaluator produces the following numeric metrics:

* **Row count:** The number of records in the evaluation dataset.
* **Prior:** The proportion of positive results in the evaluation dataset. The prior is *not* a direct measure of model performance but can be useful to contextualize other metrics.
* **Accuracy:** The proportion of records that were correctly categorized. A model's accuracy ranges between 0 and 1, where an accuracy of 1 represents a model that is correctly categorizing every record in the evaluation dataset.
* **Precision:** The number of true positives divided by the sum of the true positives and false positives. This will range between 0 and 1, where 1 is perfect model precision.
* **Recall:** The total number of true positives divided by the sum of the true positives and false negatives. This will range between 0 and 1, where 1 is perfect model recall. Also known as *sensitivity*.
* **F1 score:** The harmonic mean of the precision and recall, calculated as the product of precision and recall divided by the sum of precision and recall. A model's F1 score ranges between 0 and 1, where 1 represents a model that is correctly categorizing every record in the evaluation dataset.
* **Matthews correlation coefficient (MCC):** A numerical measure of model performance that combines the numeric correlation between the label and prediction values for each of the two classification classes equally. As a result, the MCC will only be high when the classifier is performing well on both classes. The MCC will range between -1 and 1, where a score of 1 is correctly categorizing every record.
* **ROC AUC score *(requires probability)*:** The area under the ROC (receiver operating characteristic) curve; AUC stands for "Area Under Curve". Ranges from 0 to 1, where a score of 1 represents a perfect model and a score of 0.5 is the expected score for a model that is randomly guessing.

The default binary classification evaluator produces the following plots:

* **Confusion matrix:** Displays the number of true positives, true negatives, false positives, and false negatives for predictions in the evaluation dataset.
* **Score distribution:** A bar chart of model predictions on the evaluation dataset.
* **Probability distribution *(requires probability)*:** A bar chart of the model output probabilities on the evaluation dataset. The bucket width is `0.05`, which results in 20 total buckets in the `[0.0, 1.0]` range.
* **ROC curve *(requires probability)*:** Plots the True Positive Rate against the False Positive Rate for different probabilities. A steeper curve generally indicates better model performance.
* **True positive rate (TPR) curve *(requires probability)*:** The TPR curve plots the true positive rate against a model's prediction probability.
* **False positive rate (FPR) curve *(requires probability)*:** The FPR curve plots the false positive rate against a model's prediction probability.
* **Lift curve *(requires probability)*:** Shows the model's lift at each probability; lift is a measure of model performance on the positive class versus random chance.

## Configuration

For full configuration instructions, see the documentation on [how to configure a model evaluation library](../model-evaluation-automatic/index.md#configure-evaluation-libraries).

### Required fields

The following fields are required for a binary classification evaluator. The expected value type for these columns is integer.

* **inference\_field:** Column that represents the prediction classification of the model. This evaluator assumes that a `1` is the positive class and a `0` is the negative class.
* **actual\_field:** Column containing values to which a model's predictions should be compared. This evaluator assumes that a `1` is the positive class and a `0` is the negative class.

### Optional fields

* **probability\_field:** This is an optional field that represents the probability of a positive prediction class. When the probability\_field is provided, the default binary classification evaluation library will produce the following metrics:
  * ROC AUC score
  * ROC curve
  * TPR curve
  * FPR curve
  * Lift curve

* **max\_samples\_for\_roc** The maximum number of samples to generate the model ROC curve on. If not provided, this will default to `200`.

![Configure binary classification of evaluator](../../../assets/bc45366d0cf4542e8fdd2d0347eae24b35cbbffd1e54fcf67160767f87cb9563.webp)