7eaddd9a54
ML - устранение неточностей и багов ML - оптимизация производительности DecisionTreeRegressor.Fit, RandomForestRegressor.Fit ML - тесты
53 lines
1.6 KiB
ObjectPascal
53 lines
1.6 KiB
ObjectPascal
// В этом примере сравниваются несколько моделей
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// на одной задаче классификации.
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uses MLABC;
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begin
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var (X, y) := Datasets.MakeMoons(
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n := 400,
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noise := 0.18,
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seed := 42
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);
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Println('Сравнение моделей классификации по кросс-валидации');
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Println;
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var logregScore := 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 treeScore := Validation.StratifiedCrossValidate(
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new DecisionTreeClassifier(maxDepth := 5, minSamplesSplit := 6, minSamplesLeaf := 3, seed := 42),
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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 forestScore := Validation.StratifiedCrossValidate(
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new RandomForestClassifier(nTrees := 100, maxDepth := 6, minSamplesSplit := 6, minSamplesLeaf := 3, seed := 42),
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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 gbScore := Validation.StratifiedCrossValidate(
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new GradientBoostingClassifier(nEstimators := 80, learningRate := 0.1, maxDepth := 3, minSamplesSplit := 6, minSamplesLeaf := 3, seed := 42),
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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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Println($'LogisticRegression: Accuracy = {logregScore:F3}');
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Println($'DecisionTreeClassifier: Accuracy = {treeScore:F3}');
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Println($'RandomForestClassifier: Accuracy = {forestScore:F3}');
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Println($'GradientBoostingClassifier: Accuracy = {gbScore:F3}');
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end.
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