38 lines
1.2 KiB
ObjectPascal
38 lines
1.2 KiB
ObjectPascal
// В этом примере сравниваются границы решений
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// дерева решений и случайного леса.
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uses MLABC, PlotML;
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begin
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var (X, y) := Datasets.MakeCircles(
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n := 300,
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noise := 0.3,
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factor := 0.5,
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flipProb := 0.08,
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scale := 3.0,
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seed := 42
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);
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var tree := new DecisionTreeClassifier(maxDepth := 6, 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 forest := new RandomForestClassifier(nTrees := 100, maxDepth := 6, minSamplesSplit := 6, minSamplesLeaf := 3, seed := 42);
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forest.Fit(X, y);
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var accForest := ClassificationMetrics.Accuracy(y, forest.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 := y;
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var fig := Plot.Grid(1, 2);
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fig[0,0].Surface(x1, x2, 80, 80, G -> tree.Predict(G), Palettes.Pastel);
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fig[0,0].Points(x1, x2, labels, size := 6);
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fig[0,0].Title := $'DecisionTree (Acc = {accTree:F3})';
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fig[0,1].Surface(x1, x2, 80, 80, G -> forest.Predict(G), Palettes.Pastel);
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fig[0,1].Points(x1, x2, labels, size := 6);
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fig[0,1].Title := $'RandomForest (Acc = {accForest:F3})';
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end.
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