7eaddd9a54
ML - устранение неточностей и багов ML - оптимизация производительности DecisionTreeRegressor.Fit, RandomForestRegressor.Fit ML - тесты
59 lines
1.8 KiB
ObjectPascal
59 lines
1.8 KiB
ObjectPascal
uses MLABC;
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function BuildPipe(features: array of string): DataPipeline;
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begin
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Result :=
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DataPipeline.Build(
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TaskKind.tkClassification,
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'Выжил',
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features,
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new Imputer(['Возраст']),
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new Imputer('Саутгемптон', ['ПортПосадки']),
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new OneHotEncoder('Пол'),
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new OneHotEncoder('ПортПосадки'),
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new StandardScaler,
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new RandomForestClassifier(nTrees := 100, maxDepth := 6, minSamplesLeaf := 3, minSamplesSplit := 6)
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);
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end;
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begin
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var ds := Datasets.TitanicRu;
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var features := ['Класс', 'Пол', 'Возраст', 'БратьяИСупруги', 'РодителиИДети', 'ЦенаБилета', 'ПортПосадки'];
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var df := ds.Data.Drop(['Id', 'Имя']);
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var (trainDf, testDf) :=
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df.StratifiedTrainTestSplit(ds.Target, testRatio := 0.2, seed := 42);
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var pipe := BuildPipe(features);
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pipe.Fit(trainDf);
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var pred := pipe.Predict(testDf);
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var y := pipe.GetEncodedLabels(testDf);
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var testAccuracy := Metrics.Accuracy(y, pred);
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var total := 0.0;
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var folds := Validation.StratifiedKFold(df.GetIntColumn('Выжил'), 5, seed := 1);
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var foldsCount := 0;
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foreach var (trainIdx, testIdx) in folds do
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begin
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var foldTrain := df.TakeRows(trainIdx);
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var foldTest := df.TakeRows(testIdx);
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var foldPipe := BuildPipe(features);
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foldPipe.Fit(foldTrain);
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var foldPred := foldPipe.Predict(foldTest);
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var foldY := foldPipe.GetEncodedLabels(foldTest);
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total += Metrics.Accuracy(foldY, foldPred);
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foldsCount += 1;
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end;
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var cvAccuracy := total / foldsCount;
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Println('Оценка RandomForestClassifier двумя способами');
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Println($'Accuracy на тестовой выборке: {testAccuracy:F3}');
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Println($'Средняя Accuracy по кросс-валидации: {cvAccuracy:F3}');
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end.
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