knnClassify#

Purpose#

Creates nearest neighbor predictions.

Format#

y_hat = knnClassify(mdl, X)#

Parameters:

mdl (struct) – A knnModel structure returned from a call to knnFit().
X_train – The training features.

Returns:

y_hat (Nx1 vector, or dataframe.) – The predicted classes.

Examples#

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