---
title: "evaluation(on:userColumn:itemColumn:ratingColumn:cutoffs:excludingObserved:)"
framework: createml
role: symbol
role_heading: Instance Method
path: "createml/mlrecommender/evaluation(on:usercolumn:itemcolumn:ratingcolumn:cutoffs:excludingobserved:)"
---

# evaluation(on:userColumn:itemColumn:ratingColumn:cutoffs:excludingObserved:)

Computes the metrics for the given testing data.

## Declaration

```swift
func evaluation(on testingData: DataFrame, userColumn: String, itemColumn: String, ratingColumn: String? = nil, cutoffs: [Int] = [1, 2, 3, 4, 5], excludingObserved: Bool = true) -> MLRecommenderMetrics
```

## Parameters

- `testingData`: A MLDataTable containing testing data.
- `userColumn`: Name of the Int or String typed column in the testing data containing user identifiers.
- `itemColumn`: Name of the Int or String typed column in the testing data containing item identifiers.
- `ratingColumn`: Name of an Int or Double typed column optionally in the testing data containing scores or ratings. The default is nil, which corresponds to no rating column.
- `cutoffs`: A list of Ints corresponding to each value at which the precision and recall will be evaluated. The default is [1,2,3,4,5].
- `excludingObserved`: Specifies whether user-item interactions observed in the training data are excluded when generating evaluation result. The default is true.

## Discussion

Discussion Let P𝑘 be a vector of the first k items recommended by the model for a particular user and let A be the set of items in the provided testingData for the same user. The “precision at cutoff k” for this user is: precision(k) = |A ∩ P𝑘| / k while “recall at cutoff k” for this user is: recall(k) = |A ∩ P𝑘| / |A| where |A ∩ P𝑘| is the number of elements in the intersection of A and P𝑘 and |A| is the number of elements in A.

## See Also

### Evaluating a recommender

- [MLRecommenderMetrics](createml/mlrecommendermetrics.md)