diff --git a/InstallerSamples/MachineLearning/01_GettingStarted/03_FirstClustering_DataFrame.pas b/InstallerSamples/MachineLearning/01_GettingStarted/03_FirstClustering_DataFrame.pas
index a0f43ada6..d7267e078 100644
--- a/InstallerSamples/MachineLearning/01_GettingStarted/03_FirstClustering_DataFrame.pas
+++ b/InstallerSamples/MachineLearning/01_GettingStarted/03_FirstClustering_DataFrame.pas
@@ -43,10 +43,10 @@ begin
   var labels := model.PredictLabels(X);
   
   // Получаем названия городов
-  var cities := df.GetStrColumn('city');
+  var cities := df.Str('city');
   
   // Получаем численность населения городов
-  var populations := df.GetFloatColumn('population');
+  var populations := df.Float('population');
 
   // Выводим, сколько кластеров нашёл алгоритм
   Println($'Число кластеров: {model.ClustersCount}');
diff --git a/InstallerSamples/MachineLearning/03_Preprocessing/03_Imputer_OrdinalEncoder.pas b/InstallerSamples/MachineLearning/03_Preprocessing/03_Imputer_OrdinalEncoder.pas
index 86e34436c..14693f64b 100644
--- a/InstallerSamples/MachineLearning/03_Preprocessing/03_Imputer_OrdinalEncoder.pas
+++ b/InstallerSamples/MachineLearning/03_Preprocessing/03_Imputer_OrdinalEncoder.pas
@@ -1,4 +1,4 @@
-// Пример табличной предобработки:
+// Пример табличной предобработки:
 // сначала заполняем пропуски в числовом столбце,
 // затем кодируем категориальный столбец числами.
 uses MLABC;
@@ -18,13 +18,13 @@ city,population,region
   Println;
 
   // Заполняем пропуск в числовом столбце средним значением
-  var imputer := new Imputer(['population']);
-  df := imputer.FitTransform(df);
+  var imp := new Imputer(['population']);
+  df := imp.FitTransform(df);
 
   // Кодируем названия регионов числами 0, 1, 2, ...
   var encoder := new OrdinalEncoder('region');
   df := encoder.FitTransform(df);
 
-  Println('После Imputer и LabelEncoder:');
+  Println('После Imputer и OrdinalEncoder:');
   df.Print;
 end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/Iris/08_ConfusionMatrix.pas b/InstallerSamples/MachineLearning/08_Datasets/Iris/08_ConfusionMatrix.pas
new file mode 100644
index 000000000..1e3bbd9e7
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/Iris/08_ConfusionMatrix.pas
@@ -0,0 +1,33 @@
+// Confusion matrix для классификации датасета Iris.
+
+uses MLABC, PlotML;
+
+begin
+  var ds := Datasets.Iris;
+
+  var (train, test) := ds.StratifiedTrainTestSplit(0.2, 42);
+
+  var Xtrain := train.Data.ToMatrix(train.Features);
+  var Xtest := test.Data.ToMatrix(test.Features);
+  
+  var scaler := new StandardScaler;
+  Xtrain := scaler.FitTransform(Xtrain);
+  Xtest := scaler.Transform(Xtest);
+
+  var encoder := new LabelEncoder;
+  var ytrain := encoder.FitTransform(train);
+  var ytest := encoder.Transform(test);
+
+  var model := new LogisticRegression;
+  model.Fit(Xtrain, ytrain);
+
+  var pred := model.Predict(Xtest);
+  
+  var cm := new ConfusionMatrix(ytest, pred);
+  var acc := Metrics.Accuracy(ytest, pred);
+
+  Plot.ConfusionMatrix(cm, encoder.Classes{, normalize := MatrixNormalization.Rows});
+  Plot.Title := $'Iris: LogisticRegression, accuracy = {acc:F3}';
+  Plot.XLabel := 'Предсказанный класс';
+  Plot.YLabel := 'Истинный класс';
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/Iris/08_ConfusionMatrix1.pas b/InstallerSamples/MachineLearning/08_Datasets/Iris/08_ConfusionMatrix1.pas
new file mode 100644
index 000000000..e29732f81
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/Iris/08_ConfusionMatrix1.pas
@@ -0,0 +1,29 @@
+// Confusion matrix для Iris с использованием DataPipeline.
+// Рекомендуемый стиль - он проще и явно показывает намерения
+
+uses MLABC, PlotML;
+
+begin
+  var ds := Datasets.Iris;
+  var (train, test) := ds.StratifiedTrainTestSplit(0.2, 42);
+
+  var pipe := DataPipeline.Build(
+    TaskKind.tkClassification,
+    ds.Target,
+    ds.Features,
+    new StandardScaler,
+    new LogisticRegression
+  );
+
+  pipe.Fit(train.Data);
+
+  var pred := pipe.Predict(test.Data);
+  var ytest := pipe.GetEncodedLabels(test.Data);
+  var cm := new ConfusionMatrix(ytest, pred);
+  var acc := Metrics.Accuracy(ytest, pred);
+
+  Plot.ConfusionMatrix(cm, pipe.GetClassLabels{, normalize := MatrixNormalization.Rows});
+  Plot.Title := $'Iris: DataPipeline, accuracy = {acc:F3}';
+  Plot.XLabel := 'Предсказанный класс';
+  Plot.YLabel := 'Истинный класс';
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/MoscowHousing/02_PriceHistogram.pas b/InstallerSamples/MachineLearning/08_Datasets/MoscowHousing/02_PriceHistogram.pas
index 53ba5c3f3..f3171fe25 100644
--- a/InstallerSamples/MachineLearning/08_Datasets/MoscowHousing/02_PriceHistogram.pas
+++ b/InstallerSamples/MachineLearning/08_Datasets/MoscowHousing/02_PriceHistogram.pas
@@ -8,6 +8,6 @@ begin
 
   Plot.Hist(price, bins := 40);
   Plot.Title := 'Распределение цен на квартиры в Москве';
-  Plot.XLabel('Цена (руб)');
-  Plot.YLabel('Количество');
-end.
\ No newline at end of file
+  Plot.XLabel := 'Цена (руб)';
+  Plot.YLabel := 'Количество';
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/MoscowHousing/03_AreaPriceScatter.pas b/InstallerSamples/MachineLearning/08_Datasets/MoscowHousing/03_AreaPriceScatter.pas
index 9b6f8788d..2f62e66f8 100644
--- a/InstallerSamples/MachineLearning/08_Datasets/MoscowHousing/03_AreaPriceScatter.pas
+++ b/InstallerSamples/MachineLearning/08_Datasets/MoscowHousing/03_AreaPriceScatter.pas
@@ -9,7 +9,7 @@ begin
 
   Plot.Points(area, price, size := 3);
 
-  Plot.XLabel('Площадь (м²)');
-  Plot.YLabel('Цена (руб)');
+  Plot.XLabel := 'Площадь (м²)';
+  Plot.YLabel := 'Цена (руб)';
   Plot.Title := 'Цена квартиры vs площадь';
-end.
\ No newline at end of file
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/MoscowHousing/04_MetroPriceScatter.pas b/InstallerSamples/MachineLearning/08_Datasets/MoscowHousing/04_MetroPriceScatter.pas
index 8618eaa1c..bd9ae6baf 100644
--- a/InstallerSamples/MachineLearning/08_Datasets/MoscowHousing/04_MetroPriceScatter.pas
+++ b/InstallerSamples/MachineLearning/08_Datasets/MoscowHousing/04_MetroPriceScatter.pas
@@ -9,7 +9,7 @@ begin
 
   Plot.Points(metro.Data.ConvertAll(x -> x*1.0), price.Data, size := 3);
 
-  Plot.XLabel('Минуты до метро');
-  Plot.YLabel('Цена (руб)');
+  Plot.XLabel := 'Минуты до метро';
+  Plot.YLabel := 'Цена (руб)';
   Plot.Title := 'Зависимость цены квартиры от расстояния до метро';
-end.
\ No newline at end of file
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/RussianCities/01_Info.pas b/InstallerSamples/MachineLearning/08_Datasets/RussianCities/01_Info.pas
index 8f5411a75..4cbb85b65 100644
--- a/InstallerSamples/MachineLearning/08_Datasets/RussianCities/01_Info.pas
+++ b/InstallerSamples/MachineLearning/08_Datasets/RussianCities/01_Info.pas
@@ -8,5 +8,7 @@ begin
   df.Schema.Println;
   df.PrintInfo;
   Println;
+  ds.Info;
+  Println;
   df.Print;
 end.
\ No newline at end of file
diff --git a/InstallerSamples/MachineLearning/08_Datasets/RussianCities/02_PointsByClaster.pas b/InstallerSamples/MachineLearning/08_Datasets/RussianCities/02_PointsByClaster.pas
index 5907f7f14..e3248cd1b 100644
--- a/InstallerSamples/MachineLearning/08_Datasets/RussianCities/02_PointsByClaster.pas
+++ b/InstallerSamples/MachineLearning/08_Datasets/RussianCities/02_PointsByClaster.pas
@@ -32,7 +32,7 @@ begin
     marker := MarkerType.Circle
   );
 
-  Plot.XLabel('Долгота');
-  Plot.YLabel('Широта');
+  Plot.XLabel := 'Долгота';
+  Plot.YLabel := 'Широта';
   Plot.Title := 'Кластеры городов России';
-end.
\ No newline at end of file
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/RussianCities/04_Clusters_DBScan.pas b/InstallerSamples/MachineLearning/08_Datasets/RussianCities/04_Clusters_DBScan.pas
index f73b6e462..e208a2b6a 100644
--- a/InstallerSamples/MachineLearning/08_Datasets/RussianCities/04_Clusters_DBScan.pas
+++ b/InstallerSamples/MachineLearning/08_Datasets/RussianCities/04_Clusters_DBScan.pas
@@ -76,9 +76,7 @@ begin
   
     var labels := model.PredictLabels(X);
   
-    var counts := new Dictionary<integer, integer>();
-    foreach var l in labels do
-      counts[l] := counts.Get(l, 0) + 1;
+    var counts := labels.EachCount;
   
     var n := df.RowCount;
 
diff --git a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/02_AgeFareScatter.pas b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/02_AgeFareScatter.pas
index 96cc90765..801f97382 100644
--- a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/02_AgeFareScatter.pas
+++ b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/02_AgeFareScatter.pas
@@ -7,10 +7,10 @@ begin
 
   var age := df.ToVector('Возраст');
   var fare := df.ToVector('ЦенаБилета');
-  var y := df.GetIntColumn('Выжил');
+  var y := df.Int('Выжил');
 
   Plot.Points(age, fare, y, size := 5);
-  Plot.XLabel('Возраст');
-  Plot.YLabel('Цена билета');
+  Plot.XLabel := 'Возраст';
+  Plot.YLabel := 'Цена билета';
   Plot.Title := 'Титаник: возраст, цена билета и выживание';
 end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/04_AgeHistogramBySurvival.pas b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/04_AgeHistogramBySurvival.pas
index 73d2a0e54..be79626cd 100644
--- a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/04_AgeHistogramBySurvival.pas
+++ b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/04_AgeHistogramBySurvival.pas
@@ -18,6 +18,6 @@ begin
 
   Plot.HistMany([diedAges,survivedAges], bins := 20, colors := [Colors.IndianRed, Colors.SteelBlue], alpha := 0.45, legend := ['не выжили','выжили']);
   Plot.Title := 'Титаник: возраст выживших (голубой) и невыживших (красный)';
-  Plot.XLabel('Возраст');
-  Plot.YLabel('Число пассажиров');
+  Plot.XLabel := 'Возраст';
+  Plot.YLabel := 'Число пассажиров';
 end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/06_ModelComparison.pas b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/06_ModelComparison.pas
index 3c434dddd..f1656991f 100644
--- a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/06_ModelComparison.pas
+++ b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/06_ModelComparison.pas
@@ -4,45 +4,39 @@ begin
   var ds := Datasets.TitanicRu;
   var df := ds.Data.Drop(['Id', 'Имя']);
 
-  // Заполняем пропуски.
-  var ageImputer := new Imputer(['Возраст']);
-  df := ageImputer.FitTransform(df);
-
-  var portImputer := new Imputer('Саутгемптон', ['ПортПосадки']);
-  df := portImputer.FitTransform(df);
-
-  // Кодируем категориальные признаки числами.
-  var sexEncoder := new OrdinalEncoder('Пол');
-  df := sexEncoder.FitTransform(df);
-
-  var portEncoder := new OrdinalEncoder('ПортПосадки');
-  df := portEncoder.FitTransform(df);
-
   var features := ['Класс', 'Пол', 'Возраст', 'БратьяИСупруги', 'РодителиИДети', 'ЦенаБилета', 'ПортПосадки'];
-  var X := df.ToMatrix(features);
-  var y := df.GetIntColumn('Выжил');
+  var target := 'Выжил';
 
-  var (Xtrain, Xtest, ytrain, ytest) := Validation.TrainTestSplit(X, y, testRatio := 0.2, seed := 42);
+  var (trainDf, testDf) :=
+    df.StratifiedTrainTestSplit(ds.Target, testRatio := 0.2, seed := 42);
 
-  var scaler := new StandardScaler;
-  scaler.Fit(Xtrain);
-  var XtrainScaled := scaler.Transform(Xtrain);
-  var XtestScaled := scaler.Transform(Xtest);
+  var prep :=
+    DataPipeline.BuildPreprocessing(
+      TaskKind.tkClassification,
+      target,
+      features,
+      new Imputer(['Возраст']),
+      new Imputer('Саутгемптон', ['ПортПосадки']),
+      new OneHotEncoder('Пол'),
+      new OneHotEncoder('ПортПосадки'),
+      new StandardScaler
+    );
 
-  var lr := new LogisticRegression(learningRate := 0.01, epochs := 2000);
-  lr.Fit(XtrainScaled, ytrain);
-  var predLR := lr.Predict(XtestScaled);
+  var pipeLR := prep.WithModel(new LogisticRegression(learningRate := 0.01, epochs := 2000));
+  pipeLR.Fit(trainDf);
+  var predLR := pipeLR.Predict(testDf);
+  var y := pipeLR.GetEncodedLabels(testDf);
 
-  var tree := new DecisionTreeClassifier(maxDepth := 5, minSamplesLeaf := 3, minSamplesSplit := 6);
-  tree.Fit(Xtrain, ytrain);
-  var predTree := tree.Predict(Xtest);
+  var pipeTree := prep.WithModel(new DecisionTreeClassifier(maxDepth := 5, minSamplesLeaf := 3, minSamplesSplit := 6));
+  pipeTree.Fit(trainDf);
+  var predTree := pipeTree.Predict(testDf);
 
-  var forest := new RandomForestClassifier(nTrees := 100, maxDepth := 6, minSamplesLeaf := 3, minSamplesSplit := 6);
-  forest.Fit(Xtrain, ytrain);
-  var predForest := forest.Predict(Xtest);
+  var pipeForest := prep.WithModel(new RandomForestClassifier(nTrees := 100, maxDepth := 6, minSamplesLeaf := 3, minSamplesSplit := 6));
+  pipeForest.Fit(trainDf);
+  var predForest := pipeForest.Predict(testDf);
 
   Println('Сравнение моделей на TitanicRu');
-  Println($'LogisticRegression:    Accuracy = {Metrics.Accuracy(ytest, predLR):F3}');
-  Println($'DecisionTreeClassifier: Accuracy = {Metrics.Accuracy(ytest, predTree):F3}');
-  Println($'RandomForestClassifier: Accuracy = {Metrics.Accuracy(ytest, predForest):F3}');
+  Println($'LogisticRegression:      Accuracy = {Metrics.Accuracy(y, predLR):F3}');
+  Println($'DecisionTreeClassifier:  Accuracy = {Metrics.Accuracy(y, predTree):F3}');
+  Println($'RandomForestClassifier:  Accuracy = {Metrics.Accuracy(y, predForest):F3}');
 end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/08_CrossValidation.pas b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/08_CrossValidation.pas
index 99aa63799..98d10f105 100644
--- a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/08_CrossValidation.pas
+++ b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/08_CrossValidation.pas
@@ -32,7 +32,7 @@ begin
   var testAccuracy := Metrics.Accuracy(y, pred);
 
   var total := 0.0;
-  var folds := Validation.StratifiedKFold(df.GetIntColumn('Выжил'), 5, seed := 1);
+  var folds := Validation.StratifiedKFold(df.Int('Выжил'), 5, seed := 1);
   var foldsCount := 0;
 
   foreach var (trainIdx, testIdx) in folds do
diff --git a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/09_FeatureImportance.pas b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/09_FeatureImportance.pas
index f9a04e8db..22c149efc 100644
--- a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/09_FeatureImportance.pas
+++ b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/09_FeatureImportance.pas
@@ -18,7 +18,7 @@ begin
 
   var features := ['Класс', 'Пол', 'Возраст', 'БратьяИСупруги', 'РодителиИДети', 'ЦенаБилета', 'ПортПосадки'];
   var X := df.ToMatrix(features);
-  var y := df.GetIntColumn('Выжил');
+  var y := df.Int('Выжил');
 
   var model := new RandomForestClassifier(nTrees := 100, maxDepth := 6, minSamplesLeaf := 3, minSamplesSplit := 6, seed := 42);
   model.Fit(X, y);
diff --git a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/10_SurvivalBySexAndClass.pas b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/10_SurvivalBySexAndClass.pas
index 6f6061c9f..6875196da 100644
--- a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/10_SurvivalBySexAndClass.pas
+++ b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/10_SurvivalBySexAndClass.pas
@@ -2,7 +2,7 @@ uses MLABC;
 
 function SurvivalRate(df: DataFrame): real;
 begin
-  Result := df.GetIntColumn('Выжил').Average;
+  Result := df.Int('Выжил').Average;
 end;
 
 begin
diff --git a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/11_ConfusionMatrix.pas b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/11_ConfusionMatrix.pas
new file mode 100644
index 000000000..8cab772a6
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/11_ConfusionMatrix.pas
@@ -0,0 +1,35 @@
+uses MLABC, PlotML;
+
+begin
+  var ds := Datasets.TitanicRu;
+  var features := ['Класс', 'Пол', 'Возраст', 'БратьяИСупруги', 'РодителиИДети', 'ЦенаБилета', 'ПортПосадки'];
+  var df := ds.Data.Drop(['Id', 'Имя']);
+
+  var (trainDf, testDf) :=
+    df.StratifiedTrainTestSplit(ds.Target, testRatio := 0.2, seed := 42);
+
+  var pipe :=
+    DataPipeline.Build(
+      TaskKind.tkClassification,
+      ds.Target,
+      features,
+      new Imputer(['Возраст']),
+      new Imputer('Саутгемптон', ['ПортПосадки']),
+      new OneHotEncoder('Пол'),
+      new OneHotEncoder('ПортПосадки'),
+      new StandardScaler,
+      new LogisticRegression(learningRate := 0.1, epochs := 2000)
+    );
+
+  pipe.Fit(trainDf);
+
+  var pred := pipe.Predict(testDf);
+  var y := pipe.GetEncodedLabels(testDf);
+  var cm := new ConfusionMatrix(y, pred);
+  var acc := Metrics.Accuracy(y, pred);
+
+  Plot.ConfusionMatrix(cm, ['не выжил', 'выжил']);
+  Plot.Title := $'TitanicRu: DataPipeline, accuracy = {acc:F3}';
+  Plot.XLabel := 'Предсказанный класс';
+  Plot.YLabel := 'Истинный класс';
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/12_PrecisionRecallF1.pas b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/12_PrecisionRecallF1.pas
new file mode 100644
index 000000000..e17648fee
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/12_PrecisionRecallF1.pas
@@ -0,0 +1,39 @@
+uses MLABC;
+
+begin
+  var ds := Datasets.TitanicRu;
+  var features := ['Класс', 'Пол', 'Возраст', 'БратьяИСупруги', 'РодителиИДети', 'ЦенаБилета', 'ПортПосадки'];
+  var df := ds.Data.Drop(['Id', 'Имя']);
+
+  var (trainDf, testDf) :=
+    df.StratifiedTrainTestSplit(ds.Target, testRatio := 0.2, seed := 42);
+
+  var pipe :=
+    DataPipeline.Build(
+      TaskKind.tkClassification,
+      ds.Target,
+      features,
+      new Imputer(['Возраст']),
+      new Imputer('Саутгемптон', ['ПортПосадки']),
+      new OneHotEncoder('Пол'),
+      new OneHotEncoder('ПортПосадки'),
+      new StandardScaler,
+      new RandomForestClassifier(nTrees := 100, maxDepth := 6, minSamplesLeaf := 3, minSamplesSplit := 6)
+    );
+
+  pipe.Fit(trainDf);
+
+  var pred := pipe.Predict(testDf);
+  var y := pipe.GetEncodedLabels(testDf);
+
+  Println('TitanicRu: Precision, Recall и F1');
+  Println;
+  Println('Precision — какая доля пассажиров, предсказанных как "выжил", действительно выжила.');
+  Println('Recall — какую долю реально выживших пассажиров модель нашла.');
+  Println('F1 — общий баланс между Precision и Recall.');
+  Println;
+  Println($'Accuracy  = {Metrics.Accuracy(y, pred):F3}');
+  Println($'Precision = {Metrics.Precision(y, pred):F3}');
+  Println($'Recall    = {Metrics.Recall(y, pred):F3}');
+  Println($'F1        = {Metrics.F1(y, pred):F3}');
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/13_MisclassifiedPassengers.pas b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/13_MisclassifiedPassengers.pas
new file mode 100644
index 000000000..8ba41850d
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/13_MisclassifiedPassengers.pas
@@ -0,0 +1,65 @@
+uses MLABC;
+
+function LabelText(v: integer): string;
+begin
+  Result := if v = 1 then 'выжил' else 'не выжил';
+end;
+
+begin
+  var ds := Datasets.TitanicRu;
+  var features := ['Класс', 'Пол', 'Возраст', 'БратьяИСупруги', 'РодителиИДети', 'ЦенаБилета', 'ПортПосадки'];
+
+  var fullDf := ds.Data;
+  var (trainFull, testFull) :=
+    fullDf.StratifiedTrainTestSplit(ds.Target, testRatio := 0.2, seed := 42);
+
+  var trainDf := trainFull.Drop(['Id', 'Имя']);
+  var testDf := testFull.Drop(['Id', 'Имя']);
+
+  var pipe :=
+    DataPipeline.Build(
+      TaskKind.tkClassification,
+      ds.Target,
+      features,
+      new Imputer(['Возраст']),
+      new Imputer('Саутгемптон', ['ПортПосадки']),
+      new OneHotEncoder('Пол'),
+      new OneHotEncoder('ПортПосадки'),
+      new StandardScaler,
+      new RandomForestClassifier(nTrees := 100, maxDepth := 6, minSamplesLeaf := 3, minSamplesSplit := 6)
+    );
+
+  pipe.Fit(trainDf);
+
+  var pred := pipe.Predict(testDf);
+  var y := pipe.GetEncodedLabels(testDf);
+  var predInt := pred.ToIntArray;
+  var yInt := y.ToIntArray;
+  var cur := testFull.GetCursor;
+
+  Println('TitanicRu: пассажиры, классифицированные неправильно');
+  Println;
+
+  var shown := 0;
+  while cur.MoveNext do
+  begin
+    var i := cur.Position;
+    if yInt[i] <> predInt[i] then
+    begin
+      var id := cur.Int('Id');
+      var name := cur.Str('Имя');
+      var sex := cur.Str('Пол');
+      var pclass := cur.Int('Класс');
+      var age := cur.Float('Возраст');
+      var fare := cur.Float('ЦенаБилета');
+      
+      Println($'Id={id}, {name}');
+      Println($'  Пол={sex}, класс={pclass}, возраст={age:F1}, билет={fare:F1}');
+      Println($'  Истина: {LabelText(yInt[i])}, прогноз: {LabelText(predInt[i])}');
+      Println;
+
+      shown += 1;
+      if shown >= 10 then break;
+    end;
+  end;
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/14_FeatureAblation.pas b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/14_FeatureAblation.pas
new file mode 100644
index 000000000..630ee4cc6
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/TitanicRu/14_FeatureAblation.pas
@@ -0,0 +1,52 @@
+uses MLABC;
+
+function BuildPipe(features: array of string): DataPipeline;
+begin
+  Result :=
+    DataPipeline.Build(
+      TaskKind.tkClassification,
+      'Выжил',
+      features,
+      new Imputer(['Возраст']),
+      new Imputer('Саутгемптон', ['ПортПосадки']),
+      new OneHotEncoder('Пол'),
+      new OneHotEncoder('ПортПосадки'),
+      new StandardScaler,
+      new RandomForestClassifier(nTrees := 100, maxDepth := 6, minSamplesLeaf := 3, minSamplesSplit := 6)
+    );
+end;
+
+function TestAccuracy(df: DataFrame; features: array of string): real;
+begin
+  var (trainDf, testDf) :=
+    df.StratifiedTrainTestSplit('Выжил', testRatio := 0.2, seed := 42);
+
+  var pipe := BuildPipe(features);
+  pipe.Fit(trainDf);
+
+  var pred := pipe.Predict(testDf);
+  var y := pipe.GetEncodedLabels(testDf);
+  Result := Metrics.Accuracy(y, pred);
+end;
+
+begin
+  var ds := Datasets.TitanicRu;
+  var df := ds.Data.Drop(['Id', 'Имя']);
+
+  var allFeatures :=
+    SetOf('Класс', 'Пол', 'Возраст', 'БратьяИСупруги', 'РодителиИДети', 'ЦенаБилета', 'ПортПосадки');
+  var noSex := (allFeatures - ['Пол']).ToArray;
+  var noClass := (allFeatures - ['Класс']).ToArray;
+
+  var accAll := TestAccuracy(df, allFeatures.ToArray);
+  var accNoSex := TestAccuracy(df, noSex);
+  var accNoClass := TestAccuracy(df, noClass);
+
+  Println('TitanicRu: влияние признаков на качество модели');
+  Println;
+  Println('Идея примера: убираем один важный признак и смотрим, насколько хуже становится классификация.');
+  Println;
+  Println($'Все признаки: Accuracy = {accAll:F3}');
+  Println($'Без признака "Пол": Accuracy = {accNoSex:F3}');
+  Println($'Без признака "Класс": Accuracy = {accNoClass:F3}');
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/UsedCars/01_Info.pas b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/01_Info.pas
new file mode 100644
index 000000000..1ca60462a
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/01_Info.pas
@@ -0,0 +1,11 @@
+uses MLABC;
+
+begin
+  Datasets.Language := 'ru';
+
+  var ds := Datasets.UsedCarsPrice;
+  ds.Info;
+  Println;
+
+  ds.Data.Print(20);
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/UsedCars/02_PriceHistogram.pas b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/02_PriceHistogram.pas
new file mode 100644
index 000000000..eebbc8219
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/02_PriceHistogram.pas
@@ -0,0 +1,12 @@
+uses MLABC, PlotML;
+
+begin
+  var ds := Datasets.UsedCarsPrice;
+  var df := ds.Data.Filter(r -> r.Float('price_k_rub') <= 6000);
+  var price := df.ToVector('price_k_rub');
+
+  Plot.Hist(price, bins := 30);
+  Plot.XLabel := 'Цена, тыс. руб.';
+  Plot.YLabel := 'Число автомобилей';
+  Plot.Title := 'UsedCars: распределение цен до 6000 тыс. руб.';
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/UsedCars/03_MileagePriceScatter.pas b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/03_MileagePriceScatter.pas
new file mode 100644
index 000000000..4f3047dcb
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/03_MileagePriceScatter.pas
@@ -0,0 +1,14 @@
+uses MLABC, PlotML;
+
+begin
+  var ds := Datasets.UsedCarsPrice;
+  var df := ds.Data.Filter(r -> r.Float('price_k_rub') <= 6000);
+
+  var mileage := df.ToVector('mileage_km');
+  var price := df.ToVector('price_k_rub');
+
+  Plot.Points(mileage, price, size := 4);
+  Plot.XLabel := 'Пробег, км';
+  Plot.YLabel := 'Цена, тыс. руб.';
+  Plot.Title := 'UsedCars: цена и пробег';
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/UsedCars/04_PairPlotByPrice.pas b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/04_PairPlotByPrice.pas
new file mode 100644
index 000000000..40dc35bf0
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/04_PairPlotByPrice.pas
@@ -0,0 +1,17 @@
+uses MLABC, PlotML;
+
+begin
+  var ds := Datasets.UsedCarsPrice;
+  var df := ds.Data.Filter(r -> r.Float('price_k_rub') <= 6000);
+
+  var names := Arr('year', 'mileage_1000km', 'l_100km', 'engineSize');
+  var X := df.ToMatrix(Arr('year', 'mileage_km', 'l_100km', 'engineSize'));
+  
+  for var i := 0 to X.RowCount - 1 do
+    X[i, 1] := X[i, 1] / 1000.0;
+  
+  var price := df.ToVector('price_k_rub');
+
+  Plot.PairPlot(X.Data, price.Data, names, bins := 6, maxPoints := 2000);
+  Plot.Title := 'UsedCars: признаки, окрашенные по цене';
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/UsedCars/05_YearPriceScatter.pas b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/05_YearPriceScatter.pas
new file mode 100644
index 000000000..128bf0e54
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/05_YearPriceScatter.pas
@@ -0,0 +1,15 @@
+uses MLABC, PlotML;
+
+begin
+  var ds := Datasets.UsedCarsPrice;
+  var df := ds.Data.Filter(r -> r.Float('price_k_rub') <= 6000);
+
+  var year := df.ToVector('year');
+  var price := df.ToVector('price_k_rub');
+  var make := df.EncodeLabels('Make');
+
+  Plot.Points(year, price, make, size := 4);
+  Plot.XLabel := 'Год выпуска';
+  Plot.YLabel := 'Цена, тыс. руб.';
+  Plot.Title := 'UsedCars: цена по годам и маркам';
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/UsedCars/06_RegressionPipeline.pas b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/06_RegressionPipeline.pas
new file mode 100644
index 000000000..40a53c00b
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/06_RegressionPipeline.pas
@@ -0,0 +1,45 @@
+uses MLABC;
+
+begin
+  var ds := Datasets.UsedCarsPrice;
+  var df := ds.Data;
+
+  var features := [
+    'model',
+    'year',
+    'transmission',
+    'mileage_km',
+    'fuelType',
+    'l_100km',
+    'engineSize',
+    'Make'
+  ];
+
+  var target := 'price_k_rub';
+
+  var (trainDf, testDf) := df.TrainTestSplit(0.2, seed := 42);
+
+  var pipe :=
+    DataPipeline.Build(
+      TaskKind.tkRegression,
+      target,
+      features,
+      new OrdinalEncoder('model'),
+      new OneHotEncoder('transmission'),
+      new OneHotEncoder('fuelType'),
+      new OneHotEncoder('Make'),
+      new StandardScaler,
+      new DecisionTreeRegressor(10)
+    );
+
+
+  pipe.Fit(trainDf);
+
+  var pred := pipe.Predict(testDf);
+  var y := testDf.ToVector(target);
+
+  Println('Прогнозирование цены автомобилей (DecisionTreeRegressor)');
+  Println($'MAE  = {Metrics.MAE(y, pred):F0}');
+  Println($'RMSE = {Metrics.RMSE(y, pred):F0}');
+  Println($'R²   = {Metrics.R2(y, pred):F3}');
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/UsedCars/07_ModelComparison.pas b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/07_ModelComparison.pas
new file mode 100644
index 000000000..8209f136b
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/07_ModelComparison.pas
@@ -0,0 +1,48 @@
+uses MLABC;
+
+begin
+  var ds := Datasets.UsedCarsPrice;
+  var df := ds.Data;
+
+  var features := [
+    'model',
+    'year',
+    'transmission',
+    'mileage_km',
+    'fuelType',
+    'l_100km',
+    'engineSize',
+    'Make'
+  ];
+
+  var target := 'price_k_rub';
+  var (trainDf, testDf) := df.TrainTestSplit(0.2, seed := 42);
+
+  var prep :=
+    DataPipeline.BuildPreprocessing(
+      TaskKind.tkRegression,
+      target,
+      features,
+      new OrdinalEncoder('model'),
+      new OneHotEncoder('transmission'),
+      new OneHotEncoder('fuelType'),
+      new OneHotEncoder('Make'),
+      new StandardScaler
+    );
+
+  var pipeTree := prep.WithModel(new DecisionTreeRegressor(12));
+
+  pipeTree.Fit(trainDf);
+  var predTree := pipeTree.Predict(testDf);
+
+  var pipeForest := prep.WithModel(new RandomForestRegressor(12, 50));
+
+  pipeForest.Fit(trainDf);
+  var predForest := pipeForest.Predict(testDf);
+
+  var y := testDf.ToVector(target);
+
+  Println('Сравнение моделей регрессии');
+  Println($'DecisionTreeRegressor:  R² = {Metrics.R2(y, predTree):F3}');
+  Println($'RandomForestRegressor:  R² = {Metrics.R2(y, predForest):F3}');
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/UsedCars/08_CrossValidation.pas b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/08_CrossValidation.pas
new file mode 100644
index 000000000..1408bdd5f
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/08_CrossValidation.pas
@@ -0,0 +1,64 @@
+uses MLABC;
+
+begin
+  var ds := Datasets.UsedCarsPrice;
+  var df := ds.Data;
+
+  var features := [
+    'model',
+    'year',
+    'transmission',
+    'mileage_km',
+    'fuelType',
+    'l_100km',
+    'engineSize',
+    'Make'
+  ];
+
+  var target := 'price_k_rub';
+  var (trainDf, testDf) := df.TrainTestSplit(0.2, seed := 42);
+
+  var prep :=
+    DataPipeline.BuildPreprocessing(
+      TaskKind.tkRegression,
+      target,
+      features,
+      new OrdinalEncoder('model'),
+      new OneHotEncoder('transmission'),
+      new OneHotEncoder('fuelType'),
+      new OneHotEncoder('Make'),
+      new StandardScaler
+    );
+
+  var pipe := prep.WithModel(new RandomForestRegressor(12, 50));
+  pipe.Fit(trainDf);
+
+  var pred := pipe.Predict(testDf);
+  var y := testDf.ToVector(target);
+  var testR2 := Metrics.R2(y, pred);
+
+  var total := 0.0;
+  var folds := Validation.KFold(df.RowCount, 5, seed := 1);
+  var foldsCount := 0;
+
+  foreach var (trainIdx, testIdx) in folds do
+  begin
+    var foldTrain := df.TakeRows(trainIdx);
+    var foldTest := df.TakeRows(testIdx);
+
+    var foldPipe := prep.WithModel(new RandomForestRegressor(12, 50));
+    foldPipe.Fit(foldTrain);
+
+    var foldPred := foldPipe.Predict(foldTest);
+    var foldY := foldTest.ToVector(target);
+
+    total += Metrics.R2(foldY, foldPred);
+    foldsCount += 1;
+  end;
+
+  var cvR2 := total / foldsCount;
+
+  Println('Оценка RandomForestRegressor двумя способами');
+  Println($'R² на тестовой выборке: {testR2:F3}');
+  Println($'Средний R² по кросс-валидации: {cvR2:F3}');
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/UsedCars/09_Residuals.pas b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/09_Residuals.pas
new file mode 100644
index 000000000..65a6d9562
--- /dev/null
+++ b/InstallerSamples/MachineLearning/08_Datasets/UsedCars/09_Residuals.pas
@@ -0,0 +1,71 @@
+uses MLABC;
+
+begin
+  var ds := Datasets.UsedCarsPrice;
+  var df := ds.Data;
+
+  // В этом примере смотрим не только на R²,
+  // но и на конкретные ошибки модели для отдельных автомобилей.
+  // Остаток = истинная цена - предсказанная цена.
+  // Если остаток положительный, модель занизила цену.
+  // Если отрицательный, модель завысила цену.
+
+  var features := [
+    'model',
+    'year',
+    'transmission',
+    'mileage_km',
+    'fuelType',
+    'l_100km',
+    'engineSize',
+    'Make'
+  ];
+
+  var target := 'price_k_rub';
+
+  var (trainDf, testDf) := df.TrainTestSplit(0.2, seed := 42);
+
+  var pipe :=
+    DataPipeline.Build(
+      TaskKind.tkRegression,
+      target,
+      features,
+      new OrdinalEncoder('model'),
+      new OneHotEncoder('transmission'),
+      new OneHotEncoder('fuelType'),
+      new OneHotEncoder('Make'),
+      new StandardScaler,
+      new RandomForestRegressor(12, 50)
+    );
+
+  pipe.Fit(trainDf);
+
+  var pred := pipe.Predict(testDf);
+  var y := testDf.ToVector(target);
+
+  var models := testDf.Str('model');
+  var makes := testDf.Str('Make');
+  var years := testDf.Int('year');
+
+  var idx := Arr(0..pred.Length - 1);
+  idx := idx.OrderByDescending(i -> Abs(y[i] - pred[i])).ToArray;
+
+  Println('UsedCars: самые большие ошибки модели');
+  Println;
+  Println('Остаток = истинная цена - предсказанная цена.');
+  Println('Положительный остаток означает, что модель занизила цену.');
+  Println('Отрицательный остаток означает, что модель завысила цену.');
+  Println;
+
+  for var k := 0 to Min(9, idx.Length - 1) do
+  begin
+    var i := idx[k];
+    var residual := y[i] - pred[i];
+
+    Println($'{makes[i]} {models[i]}, {years[i]}');
+    Println($'  Истинная цена: {y[i]:F0} тыс. руб.');
+    Println($'  Прогноз:       {pred[i]:F0} тыс. руб.');
+    Println($'  Остаток:       {residual:F0} тыс. руб.');
+    Println;
+  end;
+end.
diff --git a/InstallerSamples/MachineLearning/08_Datasets/_Synthetic/03_MakeRegression.pas b/InstallerSamples/MachineLearning/08_Datasets/_Synthetic/03_MakeRegression.pas
index 86ecce750..4c1bc95f1 100644
--- a/InstallerSamples/MachineLearning/08_Datasets/_Synthetic/03_MakeRegression.pas
+++ b/InstallerSamples/MachineLearning/08_Datasets/_Synthetic/03_MakeRegression.pas
@@ -13,8 +13,8 @@ begin
 
   // --- визуализация
   Plot.Title('MakeRegression синтетический датасет');
-  Plot.XLabel('признак');
-  Plot.YLabel('целевая переменная');
+  Plot.XLabel := 'признак';
+  Plot.YLabel := 'целевая переменная';
 
   Plot.Points(X.Col(0), y.ToArray, size := 3);
-end.
\ No newline at end of file
+end.
diff --git a/InstallerSamples/MachineLearning/09_Visualization/01_Scatter_Iris.pas b/InstallerSamples/MachineLearning/09_Visualization/01_Scatter_Iris.pas
index 76e41786d..3c165e33d 100644
--- a/InstallerSamples/MachineLearning/09_Visualization/01_Scatter_Iris.pas
+++ b/InstallerSamples/MachineLearning/09_Visualization/01_Scatter_Iris.pas
@@ -1,4 +1,4 @@
-// В этом примере строится scatter plot для двух признаков датасета Iris.
+// В этом примере строится scatter plot для двух признаков датасета Iris.
 //
 // По оси X откладывается длина чашелистика,
 // по оси Y — ширина чашелистика.
diff --git a/InstallerSamples/MachineLearning/09_Visualization/08_PCA_Iris.pas b/InstallerSamples/MachineLearning/09_Visualization/08_PCA_Iris.pas
new file mode 100644
index 000000000..587422883
--- /dev/null
+++ b/InstallerSamples/MachineLearning/09_Visualization/08_PCA_Iris.pas
@@ -0,0 +1,20 @@
+uses MLABC, PlotML;
+
+begin
+  var ds := Datasets.Iris;
+  var X := ds.Data.ToMatrix(ds.Features);
+  var y := ds.Data.EncodeLabels(ds.Target);
+
+  var scaler := new StandardScaler;
+  scaler.Fit(X);
+  X := scaler.Transform(X);
+
+  var pca := new PCATransformer(2);
+  pca.Fit(X);
+  var X2 := pca.Transform(X);
+
+  Plot.Points(X2.Col(0), X2.Col(1), y, size := 5);
+  Plot.XLabel := 'Первая главная компонента';
+  Plot.YLabel := 'Вторая главная компонента';
+  Plot.Title := 'Iris: проекция на две главные компоненты';
+end.
diff --git a/InstallerSamples/MachineLearning/09_Visualization/09_Bars.pas b/InstallerSamples/MachineLearning/09_Visualization/09_Bars.pas
new file mode 100644
index 000000000..79840a9c8
--- /dev/null
+++ b/InstallerSamples/MachineLearning/09_Visualization/09_Bars.pas
@@ -0,0 +1,25 @@
+uses PlotML;
+
+begin
+  var fig := Plot.Grid(2, 2);
+
+  var labels := ['sedan', 'suv', 'hatchback', 'wagon'];
+
+  fig[0, 0].Bar(labels, [120.0, 95.0, 70.0, 40.0]);
+  fig[0, 0].Title := 'Cars A';
+  fig[0, 0].XLabel := 'count';
+
+  fig[0, 1].Bar(labels, [80.0, 110.0, 55.0, 30.0]);
+  fig[0, 1].Title := 'Cars B';
+  fig[0, 1].XLabel := 'count';
+
+  fig[1, 0].Bar(labels, [45.0, 65.0, 100.0, 25.0]);
+  fig[1, 0].Title := 'Cars C';
+  fig[1, 0].XLabel := 'count';
+
+  fig[1, 1].Bar(labels, [30.0, 50.0, 75.0, 90.0]);
+  fig[1, 1].Title := 'Cars D';
+  fig[1, 1].XLabel := 'count';
+
+  Plot.Title := 'Bar plots 2x2';
+end.
diff --git a/InstallerSamples/MachineLearning/09_Visualization/10_ConfusionMatrix.pas b/InstallerSamples/MachineLearning/09_Visualization/10_ConfusionMatrix.pas
new file mode 100644
index 000000000..42e55c03b
--- /dev/null
+++ b/InstallerSamples/MachineLearning/09_Visualization/10_ConfusionMatrix.pas
@@ -0,0 +1,28 @@
+// В этом примере строится confusion matrix для классификации Iris.
+
+uses MLABC, PlotML;
+
+begin
+  var ds := Datasets.Iris;
+
+  var (train, test) := ds.StratifiedTrainTestSplit(0.2, 42);
+
+  var Xtrain := train.Data.ToMatrix(train.Features);
+  var Xtest := test.Data.ToMatrix(test.Features);
+
+  var encoder := new LabelEncoder;
+  var ytrain := encoder.FitTransform(train);
+  var ytest := encoder.Transform(test);
+
+  var model := new LogisticRegression;
+  model.Fit(Xtrain, ytrain);
+
+  var pred := model.Predict(Xtest);
+  var cm := new ConfusionMatrix(ytest, pred);
+  var acc := Metrics.Accuracy(ytest, pred);
+
+  Plot.ConfusionMatrix(cm, encoder.Classes, normalize := MatrixNormalization.Rows);
+  Plot.Title := $'Iris: LogisticRegression, accuracy = {acc:F3}';
+  Plot.XLabel := 'Predicted';
+  Plot.YLabel := 'Actual';
+end.
diff --git a/InstallerSamples/MachineLearning/09_Visualization/11_ConfusionMatrices.pas b/InstallerSamples/MachineLearning/09_Visualization/11_ConfusionMatrices.pas
new file mode 100644
index 000000000..e1c330f1d
--- /dev/null
+++ b/InstallerSamples/MachineLearning/09_Visualization/11_ConfusionMatrices.pas
@@ -0,0 +1,49 @@
+uses PlotML;
+
+begin
+  var labels := ['cat', 'dog', 'fox'];
+
+  var m1 := Matr(
+    [3.0, 0, 0],
+    [1.0, 2, 1],
+    [0.0, 1, 2]
+  );
+
+  var m2 := Matr(
+    [4.0, 1, 0],
+    [0.0, 3, 1],
+    [1.0, 0, 2]
+  );
+
+  var m3 := Matr(
+    [2.0, 1, 1],
+    [0.0, 4, 0],
+    [1.0, 1, 3]
+  );
+
+  var m4 := Matr(
+    [5.0, 0, 1],
+    [1.0, 3, 0],
+    [0.0, 2, 4]
+  );
+
+  var fig := Plot.Grid(2, 2);
+
+  fig[0, 0].ConfusionMatrix(m1, labels);
+  fig[0, 0].Title := 'Model A';
+  fig[0, 0].YLabel := 'Actual';
+
+  fig[0, 1].ConfusionMatrix(m2, labels);
+  fig[0, 1].Title := 'Model B';
+
+  fig[1, 0].ConfusionMatrix(m3, labels);
+  fig[1, 0].Title := 'Model C';
+  fig[1, 0].XLabel := 'Predicted';
+  fig[1, 0].YLabel := 'Actual';
+
+  fig[1, 1].ConfusionMatrix(m4, labels);
+  fig[1, 1].Title := 'Model D';
+  fig[1, 1].XLabel := 'Predicted';
+
+  Plot.Title := 'Confusion Matrices';
+end.