51 lines
1.2 KiB
ObjectPascal
51 lines
1.2 KiB
ObjectPascal
|
// В этом примере подбирается число соседей k
|
|||
|
|
// для KNNClassifier с помощью GridSearch.
|
|||
|
|
|
|||
|
|
uses MLABC;
|
|||
|
|
|
|||
|
|
begin
|
|||
|
|
var (X, y) := Datasets.MakeMoons(
|
|||
|
|
n := 400,
|
|||
|
|
noise := 0.18,
|
|||
|
|
seed := 42
|
|||
|
|
);
|
|||
|
|
|
|||
|
|
var scaler := new StandardScaler;
|
|||
|
|
scaler.Fit(X);
|
|||
|
|
X := scaler.Transform(X);
|
|||
|
|
|
|||
|
|
var kValues := [1, 3, 5, 7, 9, 11, 15];
|
|||
|
|
|
|||
|
|
Println('Подбор параметра k для KNNClassifier');
|
|||
|
|
Println;
|
|||
|
|
Println('Проверяемые значения k:');
|
|||
|
|
|
|||
|
|
foreach var k in kValues do
|
|||
|
|
begin
|
|||
|
|
var score := Validation.StratifiedCrossValidate(
|
|||
|
|
new KNNClassifier(k, KNNWeighting.Distance),
|
|||
|
|
X, y,
|
|||
|
|
5,
|
|||
|
|
ClassificationMetrics.Accuracy,
|
|||
|
|
seed := 42
|
|||
|
|
);
|
|||
|
|
|
|||
|
|
Println($' k = {k,2} -> средняя Accuracy = {score:F3}');
|
|||
|
|
end;
|
|||
|
|
|
|||
|
|
var (bestK, bestScore, bestModel) := GridSearch.Search(
|
|||
|
|
k -> new KNNClassifier(k, KNNWeighting.Distance),
|
|||
|
|
kValues,
|
|||
|
|
X, y,
|
|||
|
|
5,
|
|||
|
|
ClassificationMetrics.Accuracy,
|
|||
|
|
maximize := True,
|
|||
|
|
stratified := True,
|
|||
|
|
seed := 42
|
|||
|
|
);
|
|||
|
|
|
|||
|
|
Println;
|
|||
|
|
Println($'Лучшее значение k: {bestK}');
|
|||
|
|
Println($'Лучшая средняя Accuracy: {bestScore:F3}');
|
|||
|
|
end.
|