Creates nearest neighbor predictions.
.. function:: y_hat = knnClassify(mdl, X)
:param mdl: A :class:`knnModel` structure returned from a call to :func:`knnFit`.
:type mdl: struct
:param X_train: The training features.
:type X: NxP matrix, or string array.
:return y_hat: The predicted classes.
:rtype y_hat: Nx1 vector, or dataframe.
new;
library gml;
// Set seed for repeatable train/test sampling
rndseed 423432;
// Get file name with full path
fname = getGAUSSHome("pkgs/gml/examples/iris.csv");
// Get predictors
X = loadd(fname, ". -Species");
// Load labels
species = loadd(fname, "Species");
// Split data into (70%) train and (30%) test sets
{ y_train, y_test, X_train, X_test } = trainTestSplit(species, X, 0.7);
/*
** Train the model
*/
// Specify number of neighbors
k = 3;
struct knnModel mdl;
mdl = knnFit(y_train, X_train, k);
/*
** Predictions on the test set
*/
y_hat = knnClassify(mdl, X_test);
/*
** Model assessment
*/
call classificationMetrics(y_test, y_hat);
The above code will print the following output:
===========================================================================
Model: KNN Target Variable: Species
Number observations: 105 Number features: 4
Num. Neighbors: 3 Number of Classes: 3
===========================================================================
KNN Classification Prediction Frequencies:
=============================================
Label Count Total % Cum. %
setosa 14 31.11 31.11
versicolor 19 42.22 73.33
virginica 12 26.67 100
Total 45 100
=============================================
===================================================
Classification metrics
===================================================
Class Precision Recall F1-score Support
setosa 1.00 1.00 1.00 14
versicolor 0.95 0.95 0.95 19
virginica 0.92 0.92 0.92 12
Macro avg 0.95 0.95 0.95 45
Weighted avg 0.96 0.96 0.96 45
Accuracy 0.96 45
.. seealso:: :func:`knnFit`, :func:`plotClasses`