knnClassify¶
Purpose¶
Creates nearest neighbor predictions.
Format¶
Examples¶
new;
library gml;
// Get file name with full path
fname = getGAUSSHome() $+ "pkgs/gml/examples/iris.csv";
// Load numeric predictors
X = loadd(fname, ". -Species");
// Load string labels
species = loaddSA(fname, "Species");
// Set seed for repeatable train/test sampling
rndseed 423432;
// Split data into (70%) train and (30%) test sets
{ y_train, y_test, X_train, X_test } = trainTestSplit(species, X, 0.7);
/*
** Train the model
*/
k = 3;
struct knnModel mdl;
mdl = knnFit(y_train, X_train, k);
/*
** Predictions on the test set
*/
y_hat = knnClassify(mdl, X_test);
print "First three predictions = " y_hat[1:3];
print "";
print "prediction accuracy = " meanc(y_hat .$== y_test);
The above code will print the following output:
First three predictions =
virginica
versicolor
setosa
prediction accuracy = 0.956
See also