90 lines
2.6 KiB
ObjectPascal
90 lines
2.6 KiB
ObjectPascal
// Линейно разделимая задача с шумом.
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// LogisticRegression показывает наилучший результат,
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// так как модель соответствует природе данных.
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//
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// DecisionTree переобучается (рваная граница) → хуже на тесте.
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// RandomForest снижает переобучение и приближается к Logistic.
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// KNN чувствителен к шуму и даёт промежуточный результат.
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//
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// Logistic Acc: 0.8733
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// Tree Acc: 0.7800
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// Forest Acc: 0.8467
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// KNN Acc: 0.8533
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uses MLABC, PlotML;
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begin
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// --- данные
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var (X, y) := Datasets.MakeClassification(
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n := 500,
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nFeatures := 2,
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nInformative := 2,
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nRedundant := 0,
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noise := 0.5,
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classSep := 2.0,
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flipProb := 0.1,
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classBalance := 0.5,
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shuffle := True,
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seed := 1
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);
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var (XTrain, XTest, yTrain, yTest) :=
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Validation.TrainTestSplit(X, y, testRatio := 0.3, seed := 1);
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// --- модели
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var logreg := new LogisticRegression;
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logreg.Fit(XTrain, yTrain);
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var tree := new DecisionTreeClassifier;
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tree.Fit(XTrain, yTrain);
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var rf := new RandomForestClassifier(100);
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rf.Fit(XTrain, yTrain);
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var knn := new KNNClassifier(5);
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knn.Fit(XTrain, yTrain);
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// --- предсказания (для визуализации)
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var yLR := logreg.Predict(X);
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var yTree := tree.Predict(X);
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var yRF := rf.Predict(X);
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var yKNN := knn.Predict(X);
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// --- test предсказания
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var yLR_test := logreg.Predict(XTest);
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var yTree_test := tree.Predict(XTest);
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var yRF_test := rf.Predict(XTest);
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var yKNN_test := knn.Predict(XTest);
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// --- метрики
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var accLR := Metrics.Accuracy(yTest, yLR_test);
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var accTree := Metrics.Accuracy(yTest, yTree_test);
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var accRF := Metrics.Accuracy(yTest, yRF_test);
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var accKNN := Metrics.Accuracy(yTest, yKNN_test);
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Println($'Logistic Acc: {accLR,0:F4}');
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Println($'Tree Acc: {accTree,0:F4}');
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Println($'Forest Acc: {accRF,0:F4}');
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Println($'KNN Acc: {accKNN,0:F4}');
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Println;
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// --- координаты
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var x1 := X.Col(0);
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var x2 := X.Col(1);
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// --- визуализация
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var fig := Plot.Grid(2, 2);
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fig[0,0].Points(x1, x2, yLR, size := 5);
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fig[0,0].Title := $'Logistic (acc={accLR,0:F3})';
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fig[0,1].Points(x1, x2, yTree, size := 5);
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fig[0,1].Title := $'Tree (acc={accTree,0:F3})';
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fig[1,0].Points(x1, x2, yRF, size := 5);
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fig[1,0].Title := $'Forest (acc={accRF,0:F3})';
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fig[1,1].Points(x1, x2, yKNN, size := 5);
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fig[1,1].Title := $'KNN (acc={accKNN,0:F3})';
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end.
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