Metrics used to evaluate a regressor’s performance.


struct MLRegressorMetrics


To understand what performance you can expect from the regressor, you start by looking at its maximumError. This high-level metric indicates your model’s worst-case performance. To get a sense for how your model performs on average, look at the rootMeanSquaredError. In both cases, you want to minimize the value and therefore the error.


Understanding the Model

var maximumError: Double

The largest absolute difference between the expected values and the model's predicted values during testing or training.

var rootMeanSquaredError: Double

A common metric used to determine the deviation between correct and predicted values.

Handling Errors

var isValid: Bool

A Boolean value indicating whether the regressor model was able to calculate metrics.

var error: Error?

The underlying error present when the metrics are invalid.

Creating Metrics

init(maximumError: Double, rootMeanSquaredError: Double)

Creates regressor metrics describing the quality of your model.

Describing Metrics

var description: String

A text representation of the regressor metrics.

var debugDescription: String

A text representation of the regressor metrics that’s suitable for output during debugging.

var playgroundDescription: Any

A description of the regressor metrics shown in a playground.

See Also

Model Accuracy

Improving Your Model’s Accuracy

Use metrics to tune the performance of your machine learning model.

struct MLClassifierMetrics

Metrics used to evaluate a classifier’s performance.