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Feature selection for regression using neighborhood component analysis (NCA)

`FeatureSelectionNCARegression`

contains the data, fitting
information, feature weights, and other model parameters of a neighborhood
component analysis (NCA) model. `fsrnca`

learns the feature
weights using a diagonal adaptation of NCA and returns an instance of
`FeatureSelectionNCARegression`

object. The
function achieves feature selection by regularizing the feature weights.

Create a `FeatureSelectionNCAClassification`

object using
`fsrnca`

.

loss | Evaluate accuracy of learned feature weights on test data |

predict | Predict responses using neighborhood component analysis (NCA) regression model |

refit | Refit neighborhood component analysis (NCA) model for regression |

Value. To learn how value classes affect copy operations, see Copying Objects.