56 lines
1.5 KiB
ObjectPascal
56 lines
1.5 KiB
ObjectPascal
|
// В этом примере сравниваются несколько моделей
|
|||
|
|
// на одной задаче регрессии.
|
|||
|
|
|
|||
|
|
uses MLABC;
|
|||
|
|
|
|||
|
|
begin
|
|||
|
|
var (X, y) := Datasets.MakeRegression(
|
|||
|
|
n := 400,
|
|||
|
|
nFeatures := 8,
|
|||
|
|
nInformative := 3,
|
|||
|
|
noise := 0.35,
|
|||
|
|
nonlinearStrength := 1.5,
|
|||
|
|
seed := 42
|
|||
|
|
);
|
|||
|
|
|
|||
|
|
Println('Сравнение моделей регрессии по кросс-валидации');
|
|||
|
|
Println;
|
|||
|
|
|
|||
|
|
var linScore := Validation.CrossValidate(
|
|||
|
|
new LinearRegression,
|
|||
|
|
X, y,
|
|||
|
|
5,
|
|||
|
|
RegressionMetrics.R2,
|
|||
|
|
seed := 42
|
|||
|
|
);
|
|||
|
|
|
|||
|
|
var treeScore := Validation.CrossValidate(
|
|||
|
|
new DecisionTreeRegressor(maxDepth := 6, minSamplesSplit := 10, minSamplesLeaf := 5, seed := 42),
|
|||
|
|
X, y,
|
|||
|
|
5,
|
|||
|
|
RegressionMetrics.R2,
|
|||
|
|
seed := 42
|
|||
|
|
);
|
|||
|
|
|
|||
|
|
var forestScore := Validation.CrossValidate(
|
|||
|
|
new RandomForestRegressor(nTrees := 120, maxDepth := 8, minSamplesSplit := 10, minSamplesLeaf := 5, seed := 42),
|
|||
|
|
X, y,
|
|||
|
|
5,
|
|||
|
|
RegressionMetrics.R2,
|
|||
|
|
seed := 42
|
|||
|
|
);
|
|||
|
|
|
|||
|
|
var gbScore := Validation.CrossValidate(
|
|||
|
|
new GradientBoostingRegressor(nEstimators := 100, learningRate := 0.1, maxDepth := 3, minSamplesSplit := 10, minSamplesLeaf := 5, seed := 42),
|
|||
|
|
X, y,
|
|||
|
|
5,
|
|||
|
|
RegressionMetrics.R2,
|
|||
|
|
seed := 42
|
|||
|
|
);
|
|||
|
|
|
|||
|
|
Println($'LinearRegression: R² = {linScore:F3}');
|
|||
|
|
Println($'DecisionTreeRegressor: R² = {treeScore:F3}');
|
|||
|
|
Println($'RandomForestRegressor: R² = {forestScore:F3}');
|
|||
|
|
Println($'GradientBoostingRegressor: R² = {gbScore:F3}');
|
|||
|
|
end.
|