45 lines
966 B
ObjectPascal
45 lines
966 B
ObjectPascal
uses MLABC;
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begin
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var ds := Datasets.UsedCarsPrice;
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var df := ds.Data;
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var features := [
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'model',
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'year',
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'transmission',
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'mileage_km',
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'fuelType',
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'l_100km',
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'engineSize',
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'Make'
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];
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var target := 'price_k_rub';
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var (trainDf, testDf) := df.TrainTestSplit(0.2, seed := 42);
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var pipe :=
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DataPipeline.BuildRegression(
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target,
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features,
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new OrdinalEncoder('model'),
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new OneHotEncoder('transmission'),
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new OneHotEncoder('fuelType'),
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new OneHotEncoder('Make'),
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new StandardScaler,
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new DecisionTreeRegressor(10)
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);
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pipe.Fit(trainDf);
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var pred := pipe.Predict(testDf);
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var y := testDf.ToVector(target);
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Println('Прогнозирование цены автомобилей (DecisionTreeRegressor)');
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Println($'MAE = {Metrics.MAE(y, pred):F0}');
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Println($'RMSE = {Metrics.RMSE(y, pred):F0}');
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Println($'R² = {Metrics.R2(y, pred):F3}');
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end.
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