Structure

MLLinearRegressor.ModelParameters

Parameters that affect the process of training a model.

Declaration

struct MLLinearRegressor.ModelParameters

Topics

Creating Parameters

init(validationData: MLDataTable?, maxIterations: Int, l1Penalty: Double, l2Penalty: Double, stepSize: Double, convergenceThreshold: Double, featureRescaling: Bool)

Creates a new set of parameters.

Accessing Parameters

var validationData: MLDataTable?
var maxIterations: Int
var l1Penalty: Double
var l2Penalty: Double
var stepSize: Double
var convergenceThreshold: Double
var featureRescaling: Bool

Describing Parameters

var description: String

A text representation of the model parameters for a linear regressor.

var debugDescription: String

A text representation of the model parameters for a linear regressor that’s suitable for output during debugging.

var playgroundDescription: Any

A description of the model parameters for a linear regressor shown in a playground.

Relationships

Conforms To

See Also

Creating and Training a Linear Regressor

init(trainingData: MLDataTable, targetColumn: String, featureColumns: [String]?, parameters: MLLinearRegressor.ModelParameters)

Creates a Linear Regressor from the feature columns in the training data to predict the values in the target column.

let modelParameters: MLLinearRegressor.ModelParameters

The underlying parameters used when training the model.

var targetColumn: String

The name of the column you selected at initialization to define which feature the regressor predicts.

var featureColumns: [String]

The names of the columns you selected at initialization to train the regressor.