7eaddd9a54
ML - устранение неточностей и багов ML - оптимизация производительности DecisionTreeRegressor.Fit, RandomForestRegressor.Fit ML - тесты
35 lines
1.1 KiB
ObjectPascal
35 lines
1.1 KiB
ObjectPascal
// В этом примере сравниваются границы решений
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// логистической регрессии и дерева решений.
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uses MLABC, PlotML;
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begin
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var (X, y) := Datasets.MakeMoons(
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n := 300,
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noise := 0.18,
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seed := 42
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);
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var logreg := new LogisticRegression(learningRate := 0.05, epochs := 1000);
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logreg.Fit(X, y);
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var accLR := ClassificationMetrics.Accuracy(y, logreg.Predict(X));
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var tree := new DecisionTreeClassifier(maxDepth := 5, minSamplesSplit := 6, minSamplesLeaf := 3, seed := 42);
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tree.Fit(X, y);
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var accTree := ClassificationMetrics.Accuracy(y, tree.Predict(X));
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var x1 := X.Col(0);
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var x2 := X.Col(1);
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var labels := LabelsToInts(y);
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var fig := Plot.Grid(1, 2);
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fig[0,0].Surface(x1, x2, 80, 80, G -> logreg.PredictLabels(G), Palettes.Pastel);
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fig[0,0].Points(x1, x2, labels, size := 6);
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fig[0,0].Title := $'LogisticRegression (Acc = {accLR:F3})';
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fig[0,1].Surface(x1, x2, 80, 80, G -> tree.PredictLabels(G), Palettes.Pastel);
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fig[0,1].Points(x1, x2, labels, size := 6);
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fig[0,1].Title := $'DecisionTreeClassifier (Acc = {accTree:F3})';
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end.
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