56 lines
1.5 KiB
ObjectPascal
56 lines
1.5 KiB
ObjectPascal
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// В этом примере SelectKBest оставляет
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// только два самых полезных признака
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// и показывает, как это влияет на качество модели.
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uses MLABC;
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begin
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var (X, y) := Datasets.MakeClassification(
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n := 200,
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nFeatures := 6,
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nInformative := 2,
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nRedundant := 0,
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noise := 0.1,
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classSep := 1.2,
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seed := 42
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);
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var featureNames := ['f1', 'f2', 'f3', 'f4', 'f5', 'f6'];
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Println('Размер до SelectKBest: ', X.RowCount, 'x', X.ColCount);
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var skb := new SelectKBest(2, FeatureScore.Correlation);
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skb.Fit(X, y);
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var X2 := skb.Transform(X);
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Println('Размер после SelectKBest: ', X2.RowCount, 'x', X2.ColCount);
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Println;
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Println('Отобранные признаки:');
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var selected := skb.SelectedFeatures;
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for var i := 0 to selected.Length - 1 do
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Println(' ', featureNames[selected[i]]);
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Println;
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var fullScore := Validation.StratifiedCrossValidate(
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new LogisticRegression(learningRate := 0.05, epochs := 1000),
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X, y,
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5,
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ClassificationMetrics.Accuracy,
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seed := 42
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);
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var reducedScore := Validation.StratifiedCrossValidate(
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new LogisticRegression(learningRate := 0.05, epochs := 1000),
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X2, y,
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5,
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ClassificationMetrics.Accuracy,
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seed := 42
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);
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Println('Сравнение качества LogisticRegression:');
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Println($' Все признаки: Accuracy = {fullScore:F3}');
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Println($' После SelectKBest: Accuracy = {reducedScore:F3}');
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end.
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