From 4fbea87b689e79cafa30b49f97945d603c40f29a Mon Sep 17 00:00:00 2001
From: Mikhalkovich Stanislav <miks@math.sfedu.ru>
Date: Fri, 12 Jun 2026 20:20:47 +0300
Subject: [PATCH] =?UTF-8?q?=D0=9D=D0=B5=D0=B1=D0=BE=D0=BB=D1=8C=D1=88?=
 =?UTF-8?q?=D0=B8=D0=B5=20=D0=BF=D1=80=D0=B0=D0=B2=D0=BA=D0=B8=20=D0=BF?=
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---
 .../01_GettingStarted/01_FirstClassification_DataFrame.pas    | 4 ++--
 InstallerSamples/MachineLearning/02_DataFrame/df22.pas        | 2 +-
 bin/Lib/MLModelsABC.pas                                       | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/InstallerSamples/MachineLearning/01_GettingStarted/01_FirstClassification_DataFrame.pas b/InstallerSamples/MachineLearning/01_GettingStarted/01_FirstClassification_DataFrame.pas
index c9d3780c1..2ce912769 100644
--- a/InstallerSamples/MachineLearning/01_GettingStarted/01_FirstClassification_DataFrame.pas
+++ b/InstallerSamples/MachineLearning/01_GettingStarted/01_FirstClassification_DataFrame.pas
@@ -22,10 +22,10 @@ begin
   var y := df.EncodeLabels(ds.Target);
 
   // Делим данные на обучающую и тестовую выборки
-  var (Xtrain, Xtest, ytrain, ytest) := Validation.TrainTestSplit(X, y, 0.2, 1);
+  var (Xtrain, Xtest, ytrain, ytest) := Validation.TrainTestSplit(X, y, 0.2, 3);
 
   // Создаём модель логистической регрессии
-  var model := new LogisticRegression;
+  var model := new LogisticRegression(0.001);
 
   // Обучаем модель на обучающей выборке
   model.Fit(Xtrain, ytrain);
diff --git a/InstallerSamples/MachineLearning/02_DataFrame/df22.pas b/InstallerSamples/MachineLearning/02_DataFrame/df22.pas
index f5a4fb7f1..e834b5385 100644
--- a/InstallerSamples/MachineLearning/02_DataFrame/df22.pas
+++ b/InstallerSamples/MachineLearning/02_DataFrame/df22.pas
@@ -1,4 +1,4 @@
-uses DataFrameABC;
+uses MLABC;
 
 begin
   var df1 := DataFrame.FromCsvText('''
diff --git a/bin/Lib/MLModelsABC.pas b/bin/Lib/MLModelsABC.pas
index 6dd9a955a..8ec00c5a3 100644
--- a/bin/Lib/MLModelsABC.pas
+++ b/bin/Lib/MLModelsABC.pas
@@ -372,7 +372,7 @@ type
       tol: real := 1e-6;
       checkConvergence: boolean := true;
       minImprovement: real := 1e-8;
-      useFastExp: boolean := True
+      useFastExp: boolean := False
     );
   
 /// Обучает модель логистической регрессии.