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.