diff --git a/bin/Lib/DataFrameABC.pas b/bin/Lib/DataFrameABC.pas
index f39bb5497..3fdf15971 100644
--- a/bin/Lib/DataFrameABC.pas
+++ b/bin/Lib/DataFrameABC.pas
@@ -347,6 +347,89 @@ type
 
 implementation
 
+uses MLExceptions;
+
+const
+  ER_COLS_NULL =
+    'cols не может быть nil!!cols cannot be nil';
+  ER_SCHEMA_NULL =
+    'schema не может быть nil!!schema cannot be nil';
+  ER_COLS_SCHEMA_MISMATCH =
+    'Количество столбцов не совпадает со схемой!!Columns count and schema mismatch';
+  ER_JOIN_KEY_TYPE_MISMATCH =
+    'Типы ключей соединения не совпадают!!Join key types mismatch';
+  ER_JOIN_KEY_NOT_FOUND =
+    'Ключ соединения "{0}" не найден!!Join key "{0}" not found';  
+  ER_JOIN_KIND_NOT_IMPLEMENTED =
+    'Тип соединения не реализован!!Join kind not implemented';
+  ER_JOIN_KEYS_LENGTH_MISMATCH =
+    'leftKeys и rightKeys должны иметь одинаковую длину!!leftKeys and rightKeys must have the same length';
+  ER_COLUMN_NOT_FOUND =
+    'Столбец "{0}" не найден!!Column not found: {0}';
+  ER_ADD_COLUMN_ROW_MISMATCH =
+    'Несоответствие числа строк при добавлении столбца!!Row count mismatch when adding column';  
+  ER_COLUMN_VALID_LENGTH_MISMATCH =
+    'Длины data и valid не совпадают при добавлении столбца!!Data and valid length mismatch when adding column';
+  ER_COLUMN_INDEX_OUT_OF_RANGE =
+    'Индекс столбца {0} вне диапазона [0..{1})!!Column index {0} out of range [0..{1})';
+  ER_NO_VALID_VALUES_COLUMN =
+    'Нет допустимых значений в столбце {0}!!No valid values in column {0}';
+  ER_SORTBY_LENGTH_MISMATCH =
+    'SortBy: длины colIndices и descending не совпадают!!SortBy: colIndices and descending length mismatch';
+  ER_UNKNOWN_COLUMN_TYPE =
+    'Неизвестный тип столбца!!Unknown column type';
+  ER_COLUMN_ALREADY_EXISTS =
+    'Столбец "{0}" уже существует!!Column "{0}" already exists';
+  ER_SCHEMA_INCONSISTENT =
+    'Несогласованная схема: столбец "{0}" имеет RowCount={1}, ожидалось {2}!!' +
+    'Schema inconsistent: column "{0}" has RowCount={1}, expected {2}';
+  ER_SCHEMA_COLUMNCOUNT_MISMATCH =
+    'Несогласованная схема: ColumnCount={0}, columns.Count={1}!!' +
+    'Schema inconsistent: ColumnCount={0}, columns.Count={1}';
+  ER_SCHEMA_COLUMN_MISSING =
+    'Несогласованная схема: столбец "{0}" отсутствует в схеме!!' +
+    'Schema inconsistent: column "{0}" missing in schema';
+  ER_SCHEMA_COLUMN_INDEX_INCONSISTENT =
+    'Несогласованная схема: GetColumnIndex("{0}")={1}, ожидалось {2}!!' +
+    'Schema inconsistent: GetColumnIndex("{0}")={1}, expected {2}';
+  ER_COLUMN_NOT_NUMERIC =
+    'Столбец не является числовым!!Column is not numeric';
+  ER_GROUPBY_UNSUPPORTED_KEY_TYPE =
+    'Неподдерживаемый тип ключа GroupBy: {0}!!Unsupported GroupBy key type: {0}';
+  ER_ZERO_VARIANCE =
+    'Нулевая дисперсия при вычислении корреляции!!Zero variance in correlation';
+  ER_NO_VALID_PAIRS =
+    'Нет допустимых пар для вычисления корреляции!!No valid pairs for correlation';
+  ER_NO_NUMERIC_COLUMNS =
+    'Нет числовых столбцов для матрицы корреляции!!No numeric columns for correlation matrix';
+  ER_ZERO_STD_STANDARDIZE =
+    'Нулевое стандартное отклонение при вызове Standardize!!Zero standard deviation in Standardize';
+  ER_INVALID_VALUE_IN_COLUMN =
+    'Недопустимое значение в столбце "{0}"!!' +
+    'Invalid value in column "{0}"';
+  ER_ZERO_STD_COLUMN =
+    'Нулевое стандартное отклонение в столбце "{0}" при StandardizeAll!!' +
+    'Zero standard deviation in column "{0}" in StandardizeAll';
+  ER_ZERO_RANGE =
+    'Нулевой диапазон при нормализации!!Zero range in normalization';
+  ER_ZERO_RANGE_COLUMN =
+    'Нулевой диапазон в столбце "{0}"!!Zero range in column "{0}"';
+  ER_QUANTILE_P_INVALID =
+    'Параметр p должен быть в диапазоне [0,1]!!Quantile p must be in [0,1]';
+  ER_NO_VALID_VALUES_QUANTILE =
+    'Нет допустимых значений для вычисления квантиля!!No valid values for quantile';
+  ER_CSV_COLUMN_COUNT_MISMATCH =
+    'Ошибка формата CSV: ожидалось {0} столбцов, получено {1}!!' +
+    'CSV format error: expected {0} columns, got {1}';
+  ER_EMPTY_CSV =
+    'CSV-файл пуст!!Empty CSV';
+  ER_CSV_UNCLOSED_QUOTE =
+    'Ошибка формата CSV: незакрытая кавычка!!CSV format error: unclosed quote';
+  ER_CSV_INVALID_BOOL =
+    'Некорректное логическое значение "{0}" в столбце "{1}"!!' +
+    'Invalid bool "{0}" in column "{1}"';
+
+
 type
   /// Класс для группировки данных
   GroupByContext = class(IGroupByContext)
@@ -392,11 +475,11 @@ end;
 constructor DataFrame.Create(cols: List<Column>; schema: DataFrameSchema);
 begin
   if cols = nil then
-    raise new System.ArgumentException('cols is nil');
+    ArgumentNullError(ER_COLS_NULL);
   if schema = nil then
-    raise new System.ArgumentException('schema is nil');
+    ArgumentNullError(ER_SCHEMA_NULL);
   if cols.Count <> schema.ColumnCount then
-    raise new System.ArgumentException('Columns count and schema mismatch');
+    ArgumentError(ER_COLS_SCHEMA_MISMATCH);
 
   self.columns := cols;
   self.fSchema := schema;
@@ -573,7 +656,7 @@ begin
   var rt := other.fSchema.ColumnTypeAt(rightKey);
 
   if lt <> rt then
-    raise new Exception('Join key types mismatch');
+    Error(ER_JOIN_KEY_TYPE_MISMATCH);
 
   // 3. типоспецифичный алгоритм (КАК РАНЬШЕ)
   case lt of
@@ -804,9 +887,8 @@ begin
 
   // 2. проверка типов ключей — через Schema
   for var i := 0 to n - 1 do
-    if fSchema.ColumnTypeAt(leftKeyIdx[i]) <>
-       other.fSchema.ColumnTypeAt(rightKeyIdx[i]) then
-      raise new Exception('Join key types mismatch');
+    if fSchema.ColumnTypeAt(leftKeyIdx[i]) <> other.fSchema.ColumnTypeAt(rightKeyIdx[i]) then
+      Error(ER_JOIN_KEY_TYPE_MISMATCH);
 
   // 3. layout'ы (как раньше)
   var leftLayout := BuildJoinKeyLayout(leftKeyIdx);
@@ -881,7 +963,7 @@ begin
   var ri := other.fSchema.IndexOf(key);
   
   if (li < 0) or (ri < 0) then
-    raise new Exception($'Join key "{key}" not found');
+    Error(ER_JOIN_KEY_NOT_FOUND, key);
   
   var leftKeyIdx  := [li];
   var rightKeyIdx := [ri];
@@ -1032,28 +1114,6 @@ begin
   Result := res;
 end;
 
-{function DataFrame.JoinInnerSingleKey(other: DataFrame; leftKey, rightKey: integer;
-  resultSchema: DataFrameSchema): DataFrame;
-begin
-  // типы ключей — ТОЛЬКО из Schema
-  var lt := fSchema.ColumnTypeAt(leftKey);
-  var rt := other.fSchema.ColumnTypeAt(rightKey);
-
-  if lt <> rt then
-    raise new Exception('Join key types mismatch');
-
-  case lt of
-    ctInt:
-      Result := JoinInnerSingleKeyInt(other, leftKey, rightKey, resultSchema);
-    ctFloat:
-      Result := JoinInnerSingleKeyFloat(other, leftKey, rightKey, resultSchema);
-    ctStr:
-      Result := JoinInnerSingleKeyStr(other, leftKey, rightKey, resultSchema);
-    ctBool:
-      Result := JoinInnerSingleKeyBool(other, leftKey, rightKey, resultSchema);
-  end;
-end;}
-
 function DataFrame.JoinInnerSingleKey(other: DataFrame; key: string): DataFrame;
 begin
   var leftKey := fSchema.IndexOf(key);
@@ -1063,7 +1123,7 @@ begin
   var rt := other.fSchema.ColumnTypeAt(rightKey);
 
   if lt <> rt then
-    raise new Exception('Join key types mismatch');
+    Error(ER_JOIN_KEY_TYPE_MISMATCH);
 
   case lt of
     ctInt:   Result := JoinInnerSingleKeyInt(other, leftKey, rightKey);
@@ -1264,9 +1324,8 @@ begin
 
   // 2. проверка типов ключей — через Schema
   for var i := 0 to n - 1 do
-    if fSchema.ColumnTypeAt(leftKeyIdx[i]) <>
-       other.fSchema.ColumnTypeAt(rightKeyIdx[i]) then
-      raise new Exception('Join key types mismatch');
+    if fSchema.ColumnTypeAt(leftKeyIdx[i]) <> other.fSchema.ColumnTypeAt(rightKeyIdx[i]) then
+      Error(ER_JOIN_KEY_TYPE_MISMATCH);
 
   // 3. строим layout'ы
   var leftLayout := BuildJoinKeyLayout(leftKeyIdx);
@@ -1344,7 +1403,7 @@ begin
     else
       exit(FullJoinMultiKey(other, keys));
 
-  raise new Exception('Join kind not implemented');
+  Error(ER_JOIN_KIND_NOT_IMPLEMENTED);
 end;
 
 function DataFrame.Join(other: DataFrame; key: string; kind: JoinKind): DataFrame;
@@ -1355,7 +1414,7 @@ end;
 function DataFrame.Join(other: DataFrame; leftKeys, rightKeys: array of string; kind: JoinKind): DataFrame;
 begin
   if leftKeys.Length <> rightKeys.Length then
-    raise new Exception('leftKeys and rightKeys must have the same length');
+    ArgumentError(ER_JOIN_KEYS_LENGTH_MISMATCH);
 
   // временно переименовываем столбцы справа
   var tmp := other;
@@ -1416,7 +1475,7 @@ begin
   for var i := 0 to columns.Count - 1 do
     if columns[i].Info.Name = name then
       exit(i);
-  raise new Exception('Column not found: ' + name);
+  Error(ER_COLUMN_NOT_FOUND, name);
 end;
 
 function DataFrame.GetCursor: DataFrameCursor :=
@@ -1439,7 +1498,7 @@ end;
 procedure DataFrame.AddIntColumn(name: string; data: array of integer; valid: array of boolean; isCategorical: boolean);
 begin
   if (columns.Count > 0) and (data.Length <> RowCount) then
-    raise new Exception('Row count mismatch');
+    DimensionError(ER_ADD_COLUMN_ROW_MISMATCH);
 
   var c := new IntColumn;
   c.Info := new ColumnInfo(name, ctInt, isCategorical);
@@ -1450,7 +1509,7 @@ begin
   else
   begin
     if valid.Length <> data.Length then
-      raise new Exception('AddIntColumn: data and valid length mismatch');
+      DimensionError(ER_COLUMN_VALID_LENGTH_MISMATCH);;
     c.IsValid := valid;
   end;
 
@@ -1461,7 +1520,7 @@ end;
 procedure DataFrame.AddFloatColumn(name: string; data: array of real; valid: array of boolean);
 begin
   if (columns.Count > 0) and (data.Length <> RowCount) then
-    raise new Exception('Row count mismatch');
+    DimensionError(ER_ADD_COLUMN_ROW_MISMATCH);
 
   var c := new FloatColumn;
   c.Info := new ColumnInfo(name, ctFloat, False);
@@ -1472,7 +1531,7 @@ begin
   else
   begin
     if valid.Length <> data.Length then
-      raise new Exception('AddFloatColumn: data and valid length mismatch');
+      DimensionError(ER_COLUMN_VALID_LENGTH_MISMATCH);
     c.IsValid := valid;
   end;
 
@@ -1483,7 +1542,7 @@ end;
 procedure DataFrame.AddStrColumn(name: string; data: array of string; valid: array of boolean; isCategorical: boolean);
 begin
   if (columns.Count > 0) and (data.Length <> RowCount) then
-    raise new Exception('Row count mismatch');
+    DimensionError(ER_ADD_COLUMN_ROW_MISMATCH);
 
   var c := new StrColumn;
   c.Info := new ColumnInfo(name, ctStr, isCategorical);
@@ -1494,7 +1553,7 @@ begin
   else
   begin
     if valid.Length <> data.Length then
-      raise new Exception('AddStrColumn: data and valid length mismatch');
+      DimensionError(ER_COLUMN_VALID_LENGTH_MISMATCH);
     c.IsValid := valid;
   end;
 
@@ -1505,7 +1564,7 @@ end;
 procedure DataFrame.AddBoolColumn(name: string; data: array of boolean; valid: array of boolean);
 begin
   if (columns.Count > 0) and (data.Length <> RowCount) then
-    raise new Exception('Row count mismatch');
+    DimensionError(ER_ADD_COLUMN_ROW_MISMATCH);
 
   var c := new BoolColumn;
   c.Info := new ColumnInfo(name, ctBool, False);
@@ -1516,7 +1575,7 @@ begin
   else
   begin
     if valid.Length <> data.Length then
-      raise new Exception('AddBoolColumn: data and valid length mismatch');
+      DimensionError(ER_COLUMN_VALID_LENGTH_MISMATCH);
     c.IsValid := valid;
   end;
 
@@ -1528,7 +1587,7 @@ end;
 procedure DataFrame.CheckColumnIndex(colIndex: integer);
 begin
   if (colIndex < 0) or (colIndex >= ColumnCount) then
-    raise new Exception('Column index out of range');
+    ArgumentOutOfRangeError(ER_COLUMN_INDEX_OUT_OF_RANGE, colIndex, ColumnCount);
 end;
 
 function DataFrame.Sum(colIndex: integer): real;
@@ -1620,7 +1679,7 @@ begin
     end;
 
   if not has then
-    raise new Exception('Min: no valid values');
+    Error(ER_NO_VALID_VALUES_COLUMN, colIndex);
 
   Result := m;
 end;
@@ -1650,7 +1709,7 @@ begin
     end;
 
   if not has then
-    raise new Exception('Max: no valid values');
+    Error(ER_NO_VALID_VALUES_COLUMN, colIndex);
 
   Result := m;
 end;
@@ -1684,7 +1743,7 @@ begin
     end;
 
   if not has then
-    raise new Exception('MinMax: no valid values');
+    Error(ER_NO_VALID_VALUES_COLUMN, colIndex);
 
   Result := (mn, mx);
 end;
@@ -1974,7 +2033,7 @@ type
 function DataFrame.SortBy(colIndices: array of integer; descending: array of boolean): DataFrame;
 begin
   if colIndices.Length <> descending.Length then
-    raise new Exception('SortBy: length mismatch');
+    ArgumentError(ER_SORTBY_LENGTH_MISMATCH);
 
   foreach var c in colIndices do
     CheckColumnIndex(c);
@@ -2172,7 +2231,7 @@ begin
       res.AddBoolColumn(src.Info.Name, data, valid);
     end
     else
-      raise new Exception('Unknown column type');
+      Error(ER_UNKNOWN_COLUMN_TYPE);
   end;
 
   Result := res;
@@ -2422,7 +2481,7 @@ end;
 function DataFrame.WithColumnInt(name: string; f: DataFrameCursor -> integer): DataFrame;
 begin
   if fSchema.HasColumn(name) then
-    raise new Exception($'Column "{name}" already exists');
+    ArgumentError(ER_COLUMN_ALREADY_EXISTS, name);
   
   var res := new DataFrame;
 
@@ -2499,7 +2558,7 @@ end;
 function DataFrame.WithColumnFloat(name: string; f: DataFrameCursor -> real): DataFrame;
 begin
   if fSchema.HasColumn(name) then
-    raise new Exception($'Column "{name}" already exists');
+    ArgumentError(ER_COLUMN_ALREADY_EXISTS, name);
   
   var res := new DataFrame;
 
@@ -2550,7 +2609,7 @@ end;
 function DataFrame.WithColumnStr(name: string; f: DataFrameCursor -> string): DataFrame;
 begin
   if fSchema.HasColumn(name) then
-    raise new Exception($'Column "{name}" already exists');
+    ArgumentError(ER_COLUMN_ALREADY_EXISTS, name);
   
   var res := new DataFrame;
 
@@ -2601,7 +2660,7 @@ end;
 function DataFrame.WithColumnBool(name: string; f: DataFrameCursor -> boolean): DataFrame;
 begin
   if fSchema.HasColumn(name) then
-    raise new Exception($'Column "{name}" already exists');
+    ArgumentError(ER_COLUMN_ALREADY_EXISTS, name);
   
   var res := new DataFrame;
 
@@ -2737,7 +2796,7 @@ function DataFrame.AddDerivedIntColumn(
 ): DataFrame;
 begin
   if Schema.HasColumn(name) then
-    raise new Exception($'Column "{name}" already exists');
+    ArgumentError(ER_COLUMN_ALREADY_EXISTS, name);
 
   var rowCount := RowCount;
   var data := new integer[rowCount];
@@ -3028,15 +3087,11 @@ begin
   var rc := columns[0].RowCount;
   for var i := 1 to columns.Count - 1 do
     if columns[i].RowCount <> rc then
-      raise new Exception(
-        $'Schema inconsistent: column "{columns[i].Info.Name}" has RowCount={columns[i].RowCount}, expected {rc}'
-      );
+      Error(ER_SCHEMA_INCONSISTENT, columns[i].Info.Name, columns[i].RowCount, rc);
 
   // --- 2. fschema.ColumnCount = columns.Count ---
   if fschema.ColumnCount <> columns.Count then
-    raise new Exception(
-      $'Schema inconsistent: ColumnCount={fschema.ColumnCount}, columns.Count={columns.Count}'
-  );
+    Error(ER_SCHEMA_COLUMNCOUNT_MISMATCH, fSchema.ColumnCount, columns.Count);
   
   // --- 3. имена уникальны и корректно индексированы ---
   for var i := 0 to columns.Count - 1 do
@@ -3044,15 +3099,11 @@ begin
     var name := columns[i].Info.Name;
 
     if not fSchema.HasColumn(name) then
-      raise new Exception(
-        $'Schema inconsistent: column "{name}" missing in schema'
-      );
+      Error(ER_SCHEMA_COLUMN_MISSING, name);
       
     var idx := GetColumnIndex(name);
     if idx <> i then
-      raise new Exception(
-        $'Schema inconsistent: GetColumnIndex("{name}")={idx}, expected {i}'
-      );
+      Error(ER_SCHEMA_COLUMN_INDEX_INCONSISTENT, name, idx, i);
   end;
 
   {$ENDIF}
@@ -3113,7 +3164,7 @@ begin
     isInt := false;
   end
   else
-    raise new Exception('Column is not numeric');
+    Error(ER_COLUMN_NOT_NUMERIC);
 end;
 
 constructor GroupByContext.Create(df: DataFrame; keyCols: array of integer);
@@ -3137,7 +3188,7 @@ begin
       case df.columns[keyColumn].Info.ColType of
         ctInt: key := cursor.Int(keyColumn);
         ctStr: key := cursor.Str(keyColumn);
-        else raise new Exception('Unsupported GroupBy key type');
+        else Error(ER_GROUPBY_UNSUPPORTED_KEY_TYPE, df.columns[keyColumn].Info.ColType);
       end;
 
       if not groups1.ContainsKey(key) then
@@ -3171,7 +3222,7 @@ begin
         case df.columns[c].Info.ColType of
           ctInt: key[i] := cursor.Int(c);
           ctStr: key[i] := cursor.Str(c);
-          else raise new Exception('Unsupported GroupBy key type');
+          else Error(ER_GROUPBY_UNSUPPORTED_KEY_TYPE, df.columns[c].Info.ColType);
         end;
       end;
 
@@ -3618,7 +3669,7 @@ begin
   var sy := df.Std(iy);
 
   if (sx = 0) or (sy = 0) then
-    raise new Exception('Zero variance in correlation');
+    Error(ER_ZERO_VARIANCE);
 
   var cur := df.GetCursor;
   var sum := 0.0;
@@ -3632,7 +3683,7 @@ begin
     end;
 
   if cnt = 0 then
-    raise new Exception('No valid pairs for correlation');
+    Error(ER_NO_VALID_PAIRS);
 
   Result := sum / (cnt * sx * sy);
 end;
@@ -3648,7 +3699,7 @@ begin
 
   var n := names.Count;
   if n = 0 then
-    raise new Exception('No numeric columns for correlation matrix');
+    Error(ER_NO_NUMERIC_COLUMNS);
 
   var res := new DataFrame;
 
@@ -3679,12 +3730,12 @@ begin
   var std := df.Std(idx);
 
   if std = 0 then
-    raise new Exception('Zero standard deviation');
+    Error(ER_ZERO_STD_STANDARDIZE);
 
   Result := df.ReplaceColumnFloat(colName, cur ->
   begin
     if not cur.IsValid(idx) then
-      raise new Exception;
+      Error(ER_INVALID_VALUE_IN_COLUMN, colName);
     Result := (cur.Float(idx) - mean) / std;
   end);
 end;
@@ -3707,7 +3758,7 @@ begin
       means[i] := df.Mean(i);
       stds[i] := df.Std(i);
       if stds[i] = 0 then
-        raise new Exception($'Zero std in column {df.fSchema.Names[i]}');
+        Error(ER_ZERO_STD_COLUMN, df.fSchema.Names[i]);
       isNumeric[i] := true;
     end;
   end;
@@ -3761,12 +3812,12 @@ begin
   var (mn, mx) := df.MinMax(idx);
 
   if mn = mx then
-    raise new Exception('Zero range in normalization');
+    Error(ER_ZERO_RANGE);
 
   Result := df.ReplaceColumnFloat(colName, cur ->
   begin
     if not cur.IsValid(idx) then
-      raise new Exception;
+      Error(ER_INVALID_VALUE_IN_COLUMN, colName);
     Result := (cur.Float(idx) - mn) / (mx - mn);
   end);
 end;
@@ -3788,7 +3839,7 @@ begin
     begin
       var (mn, mx) := df.MinMax(i);
       if mn = mx then
-        raise new Exception($'Zero range in column {df.fSchema.Names[i]}');
+        Error(ER_ZERO_RANGE_COLUMN, df.fSchema.Names[i]);
       mins[i] := mn;
       maxs[i] := mx;
       isNumeric[i] := true;
@@ -3841,7 +3892,7 @@ end;
 static function Statistics.Quantile(df: DataFrame; colName: string; p: real): real;
 begin
   if (p < 0) or (p > 1) then
-    raise new Exception('Quantile p must be in [0,1]');
+    ArgumentError(ER_QUANTILE_P_INVALID);
 
   var idx := df.ColumnIndex(colName);
   var values := new List<real>;
@@ -3852,7 +3903,7 @@ begin
       values.Add(cur.Float(idx));
 
   if values.Count = 0 then
-    raise new Exception('No valid values for quantile');
+    Error(ER_NO_VALID_VALUES_QUANTILE);
 
   values.Sort;
 
@@ -4320,7 +4371,7 @@ begin
   
     if parts.Length <> colCount then
       if strict then
-        raise new Exception($'CSV format error: expected {colCount} columns, got {parts.Length}');
+        Error(ER_CSV_COLUMN_COUNT_MISMATCH, colCount, parts.Length);
   
     for var j := 0 to colCount - 1 do
     begin
@@ -4347,7 +4398,7 @@ begin
   end;
   
   if headers = nil then
-    raise new Exception('Empty CSV');
+    Error(ER_EMPTY_CSV);
 
   // ---------- PASS 2: allocate ----------
   var df := new DataFrame;
@@ -4394,7 +4445,7 @@ begin
     if unclosedQuote then
     begin
       if strict then
-        raise new Exception('CSV format error: unclosed quote');
+        Error(ER_CSV_UNCLOSED_QUOTE);
     
       for var j := 0 to colCount - 1 do
         valid[j][row] := false;
@@ -4404,7 +4455,7 @@ begin
     end;
     
     if (actualCount <> colCount) and strict then
-      raise new Exception($'CSV format error: expected {colCount} columns, got {actualCount}');
+      Error(ER_CSV_COLUMN_COUNT_MISMATCH, colCount, actualCount);
   
     for var j := 0 to colCount - 1 do
     begin
@@ -4439,8 +4490,7 @@ begin
         else
         begin
           if strict then
-            raise new Exception(
-              $'Invalid bool "{line.Substring(starts[j]-1, lens[j])}" in column {headers[j]}');
+            Error(ER_CSV_INVALID_BOOL, line.Substring(starts[j]-1, lens[j]), headers[j]);
           valid[j][row] := false;
         end;
       end
diff --git a/bin/Lib/DataFrameABCCore.pas b/bin/Lib/DataFrameABCCore.pas
index 6d365812d..78a96a78b 100644
--- a/bin/Lib/DataFrameABCCore.pas
+++ b/bin/Lib/DataFrameABCCore.pas
@@ -230,6 +230,36 @@ type
   
 implementation
 
+uses MLExceptions;
+
+const
+  ER_COLUMN_NOT_INT =
+    'Столбец не является Int!!Column is not Int';
+  ER_COLUMN_NOT_FLOAT =
+    'Столбец не является Float!!Column is not Float';
+  ER_COLUMN_NOT_STR =
+    'Столбец не является Str!!Column is not Str';
+  ER_COLUMN_NOT_BOOL =
+    'Столбец не является Bool!!Column is not Bool';
+  ER_DUPLICATE_COLUMN_NAME =
+    'Повторяющееся имя столбца "{0}"!!Duplicate column name "{0}"';
+  ER_NAMES_NULL =
+    'names не может быть nil!!names is nil';
+  ER_TYPES_NULL =
+    'types не может быть nil!!types is nil';
+  ER_NAMES_TYPES_LENGTH_MISMATCH =
+    'Длины names и types не совпадают!!names and types length mismatch';
+  ER_ISCATEGORICAL_LENGTH_MISMATCH =
+    'Длина isCategorical не совпадает с names!!isCategorical length mismatch';
+  ER_COLUMN_NOT_EXISTS =
+    'Столбец "{0}" не существует!!Column "{0}" does not exist';
+  ER_COLUMN_ALREADY_EXISTS =
+    'Столбец "{0}" уже существует!!Column "{0}" already exists';
+  ER_INDEX_OUT_OF_RANGE =
+    'Индекс {0} вне диапазона [0..{1})!!Index {0} out of range [0..{1})';
+  ER_INDICES_NULL =
+    'indices не может быть nil!!indices is nil';
+
 //-----------------------------
 //      Сервисные функции
 //-----------------------------
@@ -237,25 +267,25 @@ implementation
 function NotInt(pos: integer): integer;
 begin
   Result := 0;
-  raise new Exception('Column is not Int');
+  Error(ER_COLUMN_NOT_INT);
 end;
 
 function NotFloat(pos: integer): real;
 begin
   Result := 0;
-  raise new Exception('Column is not Float');
+  Error(ER_COLUMN_NOT_FLOAT);
 end;
 
 function NotStr(pos: integer): string;
 begin
   Result := nil;
-  raise new Exception('Column is not Str');
+  Error(ER_COLUMN_NOT_STR);
 end;
 
 function NotBool(pos: integer): boolean;
 begin
   Result := False;
-  raise new Exception('Column is not Bool');
+  Error(ER_COLUMN_NOT_BOOL);
 end;
 
 //-----------------------------
@@ -267,7 +297,7 @@ begin
   for var i := 0 to names.Length - 1 do
   begin
     if Result.ContainsKey(names[i]) then
-      raise new System.ArgumentException($'Duplicate column name "{names[i]}"');
+      ArgumentError(ER_DUPLICATE_COLUMN_NAME, names[i]);
     Result.Add(names[i], i);
   end;
 end;
@@ -275,12 +305,14 @@ end;
 constructor DataFrameSchema.Create(names: array of string; types: array of ColumnType;
   isCategorical: array of boolean);
 begin
-  if names = nil then raise new System.ArgumentException('names is nil');
-  if types = nil then raise new System.ArgumentException('types is nil');
+  if names = nil then 
+    ArgumentNullError(ER_NAMES_NULL);
+  if types = nil then 
+    ArgumentNullError(ER_TYPES_NULL);
   if names.Length <> types.Length then
-    raise new System.ArgumentException('names and types length mismatch');
+    ArgumentError(ER_NAMES_TYPES_LENGTH_MISMATCH);
   if (isCategorical <> nil) and (isCategorical.Length <> names.Length) then
-    raise new System.ArgumentException('isCategorical length mismatch');
+    ArgumentError(ER_ISCATEGORICAL_LENGTH_MISMATCH);
 
   fNames := Copy(names);
   fTypes := Copy(types);
@@ -293,7 +325,7 @@ end;
 function DataFrameSchema.IndexOf(name: string): integer;
 begin
   if not fIndexByName.ContainsKey(name) then
-    raise new System.ArgumentException($'Column "{name}" does not exist');
+    ArgumentError(ER_COLUMN_NOT_EXISTS, name);
   Result := fIndexByName[name];
 end;
 
@@ -303,21 +335,21 @@ function DataFrameSchema.HasColumn(name: string): boolean :=
 function DataFrameSchema.NameAt(i: integer): string;
 begin
   if (i < 0) or (i >= ColumnCount) then
-    raise new System.ArgumentOutOfRangeException('i');
+    ArgumentOutOfRangeError(ER_INDEX_OUT_OF_RANGE, i, ColumnCount);
   Result := fNames[i];
 end;
 
 function DataFrameSchema.ColumnTypeAt(i: integer): ColumnType;
 begin
   if (i < 0) or (i >= ColumnCount) then
-    raise new System.ArgumentOutOfRangeException('i');
+    ArgumentOutOfRangeError(ER_INDEX_OUT_OF_RANGE, i, ColumnCount);
   Result := fTypes[i];
 end;
 
 function DataFrameSchema.IsCategoricalAt(i: integer): boolean;
 begin
   if (i < 0) or (i >= ColumnCount) then
-    raise new System.ArgumentOutOfRangeException('i');
+    ArgumentOutOfRangeError(ER_INDEX_OUT_OF_RANGE, i, ColumnCount);
   if fIsCategorical = nil then
     Result := false
   else
@@ -326,7 +358,8 @@ end;
 
 function DataFrameSchema.Select(indices: array of integer): DataFrameSchema;
 begin
-  if indices = nil then raise new System.ArgumentException('indices is nil');
+  if indices = nil then 
+    ArgumentNullError(ER_INDICES_NULL);
 
   var n := indices.Length;
   var names := new string[n];
@@ -348,13 +381,14 @@ end;
 
 function DataFrameSchema.Drop(indices: array of integer): DataFrameSchema;
 begin
-  if indices = nil then raise new System.ArgumentException('indices is nil');
+  if indices = nil then 
+    ArgumentNullError(ER_INDICES_NULL);
 
   var drop := new boolean[ColumnCount];
   foreach var i in indices do
   begin
     if (i < 0) or (i >= ColumnCount) then
-      raise new System.ArgumentOutOfRangeException('indices');
+      ArgumentOutOfRangeError(ER_INDEX_OUT_OF_RANGE, i, ColumnCount);
     drop[i] := true;
   end;
 
@@ -369,9 +403,9 @@ end;
 function DataFrameSchema.Rename(oldName, newName: string): DataFrameSchema;
 begin
   if not HasColumn(oldName) then
-    raise new System.ArgumentException($'Column "{oldName}" does not exist');
+    ArgumentError(ER_COLUMN_NOT_EXISTS, oldName);
   if HasColumn(newName) then
-    raise new System.ArgumentException($'Column "{newName}" already exists');
+    ArgumentError(ER_COLUMN_ALREADY_EXISTS, newName);
 
   var names := Copy(fNames);
   names[IndexOf(oldName)] := newName;
@@ -393,8 +427,10 @@ end;
 class function DataFrameSchema.Merge(left, right: DataFrameSchema;
   leftKeys, rightKeys: array of integer; rightPrefix: string): DataFrameSchema;
 begin
-  if left = nil then raise new System.ArgumentException('left is nil');
-  if right = nil then raise new System.ArgumentException('right is nil');
+  if left = nil then 
+    raise new System.ArgumentException('left is nil');
+  if right = nil then 
+    raise new System.ArgumentException('right is nil');
   if leftKeys.Length <> rightKeys.Length then
     raise new System.ArgumentException('join keys length mismatch');
 
diff --git a/bin/Lib/MLABC.pas b/bin/Lib/MLABC.pas
index ca85b2180..579f5fa7f 100644
--- a/bin/Lib/MLABC.pas
+++ b/bin/Lib/MLABC.pas
@@ -50,6 +50,13 @@ type
   
 implementation
 
+const
+  ER_TO_MATRIX_NO_COLUMNS =
+    'ToMatrix: не указаны столбцы!!ToMatrix: no columns specified';
+  ER_TO_VECTOR_NON_NUMERIC =
+    'ToVector: столбец "{0}" содержит нечисловые или NA значения!!' +
+    'ToVector: column "{0}" contains non-numeric or NA values';  
+
 function ToMatrix(Self: DataFrame; colNames: array of string): Matrix; extensionmethod;
 begin
   var df := Self;
@@ -57,7 +64,7 @@ begin
   var p := colNames.Length;
 
   if p = 0 then
-    raise new Exception('ToMatrix: no columns specified');
+    ArgumentError(ER_TO_MATRIX_NO_COLUMNS);
 
   Result := new Matrix(n, p);
 
@@ -92,9 +99,7 @@ begin
     var value: real;
 
     if not col.TryGetNumericValue(i, value) then
-      raise new Exception(
-        'ToVector: column "' + colName +
-        '" contains non-numeric or NA values');
+      ArgumentError(ER_TO_VECTOR_NON_NUMERIC, colName);
 
     Result[i] := value;
   end;
diff --git a/bin/Lib/MLExceptions.pas b/bin/Lib/MLExceptions.pas
index 0277fb397..5ad37d2d3 100644
--- a/bin/Lib/MLExceptions.pas
+++ b/bin/Lib/MLExceptions.pas
@@ -2,13 +2,24 @@
 
 interface
 
+const
+  ER_DIM_MISMATCH =
+  'Несоответствие размерностей: {0} и {1}!!Dimension mismatch: {0} and {1}';  
+  ER_PARAM_VALUES_EMPTY =
+    'Список paramValues пуст!!paramValues is empty';
+  ER_EMPTY_DATASET =
+    'Набор данных пуст!!Dataset is empty';
+  ER_LAMBDA_NEGATIVE =
+    'Параметр lambda должен быть >= 0!!Lambda parameter must be >= 0';  
+  ER_FIT_NOT_CALLED =
+    'Метод Fit() не вызван!!Fit() must be called first';
+  ER_MODEL_NULL =
+    'Модель не может быть nil!!Model cannot be nil';
+
 type
   /// Базовое исключение ML-библиотеки
   MLException = class(Exception);
 
-  /// Ошибка аргументов ML-методов
-  MLArgumentException = class(System.ArgumentException);
-
   /// Попытка использования необученной модели/пайплайна
   MLNotFittedException = class(MLException);
 
@@ -19,6 +30,8 @@ procedure Error(msg: string; params args: array of object);
 procedure ArgumentError(msg: string; params args: array of object);
 procedure NotFittedError(msg: string; params args: array of object);
 procedure DimensionError(msg: string; params args: array of object);
+procedure ArgumentNullError(msg: string; params args: array of object);
+procedure ArgumentOutOfRangeError(msg: string; params args: array of object);
 
 implementation
 
@@ -39,7 +52,7 @@ end;
 
 procedure ArgumentError(msg: string; params args: array of object);
 begin
-  raise new MLArgumentException(FormatSafe(msg, args));
+  raise new System.ArgumentException(FormatSafe(msg, args));
 end;
 
 procedure NotFittedError(msg: string; params args: array of object);
@@ -52,4 +65,14 @@ begin
   raise new MLDimensionException(FormatSafe(msg, args));
 end;
 
+procedure ArgumentNullError(msg: string; params args: array of object);
+begin
+  raise new System.ArgumentNullException(FormatSafe(msg, args));
+end;
+
+procedure ArgumentOutOfRangeError(msg: string; params args: array of object);
+begin
+  raise new System.ArgumentOutOfRangeException(FormatSafe(msg, args));
+end;
+
 end.
diff --git a/bin/Lib/MLModelsABC.pas b/bin/Lib/MLModelsABC.pas
index bad6be643..72d6064a1 100644
--- a/bin/Lib/MLModelsABC.pas
+++ b/bin/Lib/MLModelsABC.pas
@@ -593,8 +593,34 @@ type
   
 implementation  
 
-uses System;
+uses MLExceptions;
 
+const
+  ER_PIPELINE_NO_STEPS =
+    'Pipeline должен содержать хотя бы один шаг!!Pipeline requires at least one step';
+  ER_PIPELINE_LAST_NOT_MODEL =
+    'Последний шаг Pipeline должен быть моделью (IModel)!!Last step must be a model (IModel)';
+  ER_PIPELINE_INVALID_STEP_ORDER =
+    'Все шаги, кроме последнего, должны быть трансформерами!!All steps except the last must be transformers';
+  ER_TRANSFORMER_NULL =
+    'Трансформер не может быть nil!!Transformer cannot be nil';
+  ER_PROBA_NOT_SUPPORTED =
+    'Модель не поддерживает предсказание вероятностей!!Model does not support probability prediction';
+  ER_RANGE_INVALID =
+    'rangeMax должен быть больше rangeMin!!rangeMax must be greater than rangeMin';
+  ER_K_MUST_BE_POSITIVE =
+    'Параметр k должен быть > 0!!Parameter k must be > 0';
+  ER_K_EXCEEDS_FEATURES =
+    'k превышает число признаков!!k exceeds feature count';  
+  ER_THRESHOLD_NEGATIVE =
+    'Порог threshold должен быть >= 0!!threshold must be >= 0';  
+  ER_CHI_SQUARE_NEGATIVE =
+    'ChiSquare требует неотрицательные признаки!!ChiSquare requires non-negative features';
+  ER_UNKNOWN_FEATURE_SCORE =
+    'Неизвестный тип FeatureScore!!Unknown FeatureScore type';
+  ER_SELECTKBEST_FIT_INVALID =
+    'Для SelectKBest необходимо вызывать Fit(X, y)!!SelectKBest requires Fit(X, y)';
+  
 //-----------------------------
 //       LinearRegression
 //-----------------------------
@@ -609,9 +635,9 @@ begin
   var n := X.RowCount;
 
   if n = 0 then
-    raise new Exception('Empty dataset');
+    ArgumentError(ER_EMPTY_DATASET);
   if y.Length <> n then
-    raise new Exception('Dimension mismatch in Fit');
+    DimensionError(ER_DIM_MISMATCH, y.Length, n);
 
   // 1. Means
   var meanX := X.ColumnMeans;
@@ -639,10 +665,10 @@ end;
 function LinearRegression.Predict(X: Matrix): Vector;
 begin
   if not ffitted then
-    raise new Exception('Model not fitted');
+    NotFittedError(ER_FIT_NOT_CALLED);
 
   if X.ColCount <> fcoef.Length then
-    raise new Exception('Dimension mismatch in Predict');
+    DimensionError(ER_DIM_MISMATCH, X.ColCount, fCoef.Length);
 
   Result := X * fcoef + intercept;
 end;
@@ -696,7 +722,7 @@ begin
   var p := X.ColCount;
 
   if y.Length <> n then
-    raise new Exception('Dimension mismatch in Fit');
+    DimensionError(ER_DIM_MISMATCH, y.Length, n);
 
   fCoef := new Vector(p);
   fIntercept := 0.0;
@@ -726,7 +752,7 @@ end;
 function LogisticRegression.PredictProba(X: Matrix): Vector;
 begin
   if not fFitted then
-    raise new Exception('Model not fitted');
+    NotFittedError(ER_FIT_NOT_CALLED);
 
   Result := Activations.Sigmoid(X * fCoef + fIntercept);
 end;
@@ -767,7 +793,7 @@ end;
 constructor RidgeRegression.Create(lambda: real);
 begin
   if lambda < 0 then
-    raise new ArgumentException('lambda must be >= 0');
+    ArgumentError(ER_LAMBDA_NEGATIVE, lambda);
   fLambda := lambda;
   fFitted := false;
 end;
@@ -775,7 +801,7 @@ end;
 function RidgeRegression.Fit(X: Matrix; y: Vector): IModel;
 begin
   if X.RowCount <> y.Length then
-    raise new ArgumentException('X and y size mismatch');
+    DimensionError(ER_DIM_MISMATCH, X.RowCount, y.Length);
 
   var n := X.RowCount;
   var p := X.ColCount;
@@ -808,7 +834,7 @@ end;
 function RidgeRegression.Predict(X: Matrix): Vector;
 begin
   if not fFitted then
-    raise new InvalidOperationException('Model is not fitted');
+    NotFittedError(ER_FIT_NOT_CALLED);
 
   Result := X * fCoef;
   for var i := 0 to Result.Length - 1 do
@@ -833,7 +859,7 @@ end;
 constructor ElasticNet.Create(lambda1, lambda2: real; maxIter: integer; tol: real);
 begin
   if (lambda1 < 0) or (lambda2 < 0) then
-    raise new ArgumentException('lambda must be >= 0');
+    ArgumentError(ER_LAMBDA_NEGATIVE);
 
   fLambda1 := lambda1;
   fLambda2 := lambda2;
@@ -855,7 +881,7 @@ end;
 function ElasticNet.Fit(X: Matrix; y: Vector): IModel;
 begin
   if X.RowCount <> y.Length then
-    raise new ArgumentException('X and y size mismatch');
+    DimensionError(ER_DIM_MISMATCH, X.RowCount, y.Length);
 
   // Loss(β) = ||y - (Xβ + b)||² + λ1||β||1 + λ2||β||2²
 
@@ -921,7 +947,7 @@ end;
 function ElasticNet.Predict(X: Matrix): Vector;
 begin
   if not fFitted then
-    raise new InvalidOperationException('Model is not fitted');
+    NotFittedError(ER_FIT_NOT_CALLED);
 
   Result := X * fCoef;
   for var i := 0 to Result.Length - 1 do
@@ -1036,7 +1062,7 @@ end;
 function MulticlassLogisticRegression.PredictProbaMatrix(X: Matrix): Matrix;
 begin
   if not fFitted then
-    raise new Exception('Model not fitted');
+    NotFittedError(ER_FIT_NOT_CALLED);
 
   var m := X.RowCount;
 
@@ -1106,20 +1132,20 @@ constructor Pipeline.Create(model: IModel);
 begin
   Create;
   if model = nil then
-    raise new ArgumentException('Model cannot be nil');
+    ArgumentError(ER_MODEL_NULL);
   fModel := model;
 end;
 
 class function Pipeline.Build(params steps: array of IPipeStep): Pipeline;
 begin
   if (steps = nil) or (Length(steps) = 0) then
-    raise new ArgumentException('Pipeline requires at least one step');
+    ArgumentError(ER_PIPELINE_NO_STEPS);
 
   // последний шаг должен быть моделью
   var last := steps[High(steps)];
 
   if not (last is IModel) then
-    raise new ArgumentException('Last step must be a model (IModel)');
+    ArgumentError(ER_PIPELINE_LAST_NOT_MODEL);
 
   var pipe := new Pipeline(last as IModel);
 
@@ -1129,7 +1155,7 @@ begin
     var step := steps[i];
 
     if not (step is ITransformer) then
-      raise new ArgumentException('All steps except the last must be transformers');
+      ArgumentError(ER_PIPELINE_INVALID_STEP_ORDER);
 
     pipe.Add(step as ITransformer);
   end;
@@ -1140,7 +1166,7 @@ end;
 function Pipeline.Add(t: ITransformer): Pipeline;
 begin
   if t = nil then
-    raise new ArgumentException('Transformer cannot be nil');
+    ArgumentError(ER_TRANSFORMER_NULL);
 
   fTransformers.Add(t);
   Result := Self;
@@ -1149,7 +1175,7 @@ end;
 function Pipeline.SetModel(m: IModel): Pipeline;
 begin
   if m = nil then
-    raise new ArgumentException('Model cannot be nil');
+    ArgumentError(ER_TRANSFORMER_NULL);
 
   fModel := m;
   Result := Self;
@@ -1158,7 +1184,7 @@ end;
 function Pipeline.Fit(X: Matrix; y: Vector): IModel;
 begin
   if fModel = nil then
-    raise new Exception('Model is not set');
+    ArgumentError(ER_MODEL_NULL);
 
   var Xt := X;
 
@@ -1180,7 +1206,7 @@ end;
 function Pipeline.Transform(X: Matrix): Matrix;
 begin
   if not fFitted then
-    raise new Exception('Pipeline not fitted');
+    NotFittedError(ER_FIT_NOT_CALLED);
 
   var Xt := X;
 
@@ -1193,7 +1219,7 @@ end;
 function Pipeline.Predict(X: Matrix): Vector;
 begin
   if not fFitted then
-    raise new Exception('Pipeline not fitted');
+    NotFittedError(ER_FIT_NOT_CALLED);
 
   var Xt := Transform(X);
   Result := fModel.Predict(Xt);
@@ -1202,10 +1228,10 @@ end;
 function Pipeline.PredictProba(X: Matrix): Vector;
 begin
   if not fFitted then
-    raise new Exception('Pipeline not fitted');
+    NotFittedError(ER_FIT_NOT_CALLED);
 
   if not (fModel is IProbabilisticClassifier) then
-    raise new Exception('Model does not support probability prediction');
+    Error(ER_PROBA_NOT_SUPPORTED);
 
   var Xt := Transform(X);
 
@@ -1234,7 +1260,7 @@ end;
 function Pipeline.Clone: IModel;
 begin
   if fModel = nil then
-    raise new InvalidOperationException('Pipeline has no model');
+    ArgumentError(ER_MODEL_NULL);
 
   var p := new Pipeline;
 
@@ -1262,7 +1288,7 @@ end;
 function StandardScaler.Transform(X: Matrix): Matrix;
 begin
   if not fFitted then
-    raise new Exception('StandardScaler not fitted');
+    Error(ER_PROBA_NOT_SUPPORTED);
 
   var n := X.RowCount;
   var p := X.ColCount;
@@ -1301,7 +1327,7 @@ end;
 constructor MinMaxScaler.Create(rangeMin: real; rangeMax: real);
 begin
   if rangeMax <= rangeMin then
-    raise new Exception('rangeMax must be greater than rangeMin');
+    ArgumentError(ER_RANGE_INVALID);
 
   fRangeMin := rangeMin;
   fRangeMax := rangeMax;
@@ -1320,7 +1346,7 @@ end;
 function MinMaxScaler.Transform(X: Matrix): Matrix;
 begin
   if not fFitted then
-    raise new Exception('MinMaxScaler is not fitted');
+    NotFittedError(ER_FIT_NOT_CALLED);
 
   var n := X.RowCount;
   var p := X.ColCount;
@@ -1362,7 +1388,7 @@ end;
 constructor PCATransformer.Create(k: integer);
 begin
   if k <= 0 then
-    raise new ArgumentException('k must be > 0');
+    ArgumentError(ER_K_MUST_BE_POSITIVE);
 
   fK := k;
   fFitted := false;
@@ -1371,7 +1397,7 @@ end;
 function PCATransformer.Fit(X: Matrix): ITransformer;
 begin
   if fK > X.ColCount then
-    raise new ArgumentException('k exceeds feature count');
+    ArgumentError(ER_K_EXCEEDS_FEATURES);
 
   fMean := X.ColumnMeans;
 
@@ -1392,7 +1418,7 @@ end;
 function PCATransformer.Transform(X: Matrix): Matrix;
 begin
   if not fFitted then
-    raise new InvalidOperationException('PCA not fitted');
+    NotFittedError(ER_FIT_NOT_CALLED);
 
   var Xc := X.Clone;
 
@@ -1420,7 +1446,7 @@ end;
 constructor VarianceThreshold.Create(threshold: real);
 begin
   if threshold < 0 then
-    raise new ArgumentException('threshold must be >= 0');
+    ArgumentError(ER_THRESHOLD_NEGATIVE);
 
   fThreshold := threshold;
   fFitted := false;
@@ -1445,7 +1471,7 @@ end;
 function VarianceThreshold.Transform(X: Matrix): Matrix;
 begin
   if not fFitted then
-    raise new InvalidOperationException('VarianceThreshold not fitted');
+    NotFittedError(ER_FIT_NOT_CALLED);
 
   var n := X.RowCount;
   var k := fSelected.Length;
@@ -1476,7 +1502,7 @@ end;
 constructor SelectKBest.Create(k: integer; score: FeatureScore);
 begin
   if k <= 0 then
-    raise new ArgumentException('k must be > 0');
+    ArgumentError(ER_K_MUST_BE_POSITIVE);
 
   fK := k;
   fScoreType := score;
@@ -1487,7 +1513,7 @@ end;
 constructor SelectKBest.Create(k: integer; scoreFunc: (Vector, Vector) -> real);
 begin
   if k <= 0 then
-    raise new ArgumentException('k must be > 0');
+    ArgumentError(ER_K_MUST_BE_POSITIVE);
 
   fK := k;
   fScoreFunc := scoreFunc;
@@ -1589,7 +1615,7 @@ begin
   begin
     var v := feature[i];
     if v < 0 then
-      raise new ArgumentException('ChiSquare requires non-negative features');
+      ArgumentError(ER_CHI_SQUARE_NEGATIVE);
 
     total += v;
 
@@ -1630,7 +1656,7 @@ begin
     FeatureScore.AnovaF: Result := ComputeAnovaF(feature, y);
     FeatureScore.ChiSquare: Result := ComputeChiSquare(feature, y);
   else
-    raise new InvalidOperationException('Unknown FeatureScore type');
+    Error(ER_UNKNOWN_FEATURE_SCORE);
   end;
 end;
 
@@ -1671,13 +1697,13 @@ end;
 function SelectKBest.Fit(X: Matrix): ITransformer;
 begin
   Result := nil;
-  raise new InvalidOperationException('Target variable is required');
+  ArgumentError(ER_SELECTKBEST_FIT_INVALID);
 end;
 
 function SelectKBest.Transform(X: Matrix): Matrix;
 begin
   if not fFitted then
-    raise new InvalidOperationException('SelectKBest not fitted');
+    NotFittedError(ER_FIT_NOT_CALLED);
 
   var n := X.RowCount;
   var k := fSelected.Length;
@@ -1731,7 +1757,7 @@ end;
 function Normalizer.Transform(X: Matrix): Matrix;
 begin
   if not fFitted then
-    raise new InvalidOperationException('Normalizer not fitted');
+    NotFittedError(ER_FIT_NOT_CALLED);
 
   var n := X.RowCount;
   var p := X.ColCount;
diff --git a/bin/Lib/MetricsABC.pas b/bin/Lib/MetricsABC.pas
index e32885714..6d9e14a15 100644
--- a/bin/Lib/MetricsABC.pas
+++ b/bin/Lib/MetricsABC.pas
@@ -138,6 +138,8 @@ type
   
 implementation  
 
+uses MLExceptions;
+
 //-----------------------------
 //           Metrics
 //-----------------------------
@@ -145,7 +147,7 @@ implementation
 static function Metrics.MSE(yTrue, yPred: Vector): real;
 begin
   if yTrue.Length <> yPred.Length then
-    raise new Exception('Dimension mismatch in MSE');
+    DimensionError(ER_DIM_MISMATCH, yTrue.Length, yPred.Length);
 
   var n := yTrue.Length;
   var s := 0.0;
@@ -162,7 +164,7 @@ end;
 static function Metrics.MAE(yTrue, yPred: Vector): real;
 begin
   if yTrue.Length <> yPred.Length then
-    raise new Exception('Dimension mismatch in MAE');
+    DimensionError(ER_DIM_MISMATCH, yTrue.Length, yPred.Length);
 
   var n := yTrue.Length;
   var s := 0.0;
@@ -176,7 +178,7 @@ end;
 static function Metrics.R2(yTrue, yPred: Vector): real;
 begin
   if yTrue.Length <> yPred.Length then
-    raise new Exception('Dimension mismatch in R2');
+    DimensionError(ER_DIM_MISMATCH, yTrue.Length, yPred.Length);
 
   var n := yTrue.Length;
   var meanY := yTrue.Mean;
@@ -202,7 +204,7 @@ end;
 static function Metrics.LogLoss(yTrue, yProb: Vector): real;
 begin
   if yTrue.Length <> yProb.Length then
-    raise new Exception('Dimension mismatch in LogLoss');
+    DimensionError(ER_DIM_MISMATCH, yTrue.Length, yProb.Length);
 
   var n := yTrue.Length;
   var eps := 1e-15;
@@ -224,7 +226,7 @@ end;
 static function Metrics.ROC(yTrue, yProb: Vector): (Vector, Vector);
 begin
   if yTrue.Length <> yProb.Length then
-    raise new Exception('Dimension mismatch in ROC');
+    DimensionError(ER_DIM_MISMATCH, yTrue.Length, yProb.Length);
 
   var n := yTrue.Length;
 
@@ -319,7 +321,7 @@ end;
 constructor ConfusionMatrix.Create(yTrue, yPred: Vector);
 begin
   if yTrue.Length <> yPred.Length then
-    raise new Exception('Dimension mismatch in ConfusionMatrix');
+    DimensionError(ER_DIM_MISMATCH, yTrue.Length, yPred.Length);
 
   for var i := 0 to yTrue.Length - 1 do
   begin
diff --git a/bin/Lib/PreprocessorABC.pas b/bin/Lib/PreprocessorABC.pas
index 3a5b68ec2..0d26d6765 100644
--- a/bin/Lib/PreprocessorABC.pas
+++ b/bin/Lib/PreprocessorABC.pas
@@ -173,6 +173,14 @@ type
 
 implementation
 
+uses MLExceptions;
+
+const
+  ER_SCALER_NO_COLUMNS =
+    'StandardScaler: столбцы не указаны!!StandardScaler: columns not specified';
+  ER_SCALER_COLUMN_NOT_NUMERIC =
+    'StandardScaler: столбец "{0}" не является числовым!!StandardScaler: column "{0}" is not numeric';  
+
 //-----------------------------
 //        StandardScaler
 //-----------------------------
@@ -180,7 +188,7 @@ implementation
 constructor DataStandardScaler.Create(params columns: array of string);
 begin
   if (columns = nil) or (columns.Length = 0) then
-    raise new ArgumentException('StandardScaler: columns not specified');
+    ArgumentError(ER_SCALER_NO_COLUMNS);
 
   cols := columns;
   fitted := false;
@@ -199,7 +207,7 @@ begin
 
     var ct := df.Schema.ColumnTypeAt(idx);
     if not (ct in [ColumnType.ctInt, ColumnType.ctFloat]) then
-      raise new Exception($'StandardScaler: column "{colName}" is not numeric');
+      Error(ER_SCALER_COLUMN_NOT_NUMERIC, colName);
 
     var sum := 0.0;
     var sum2 := 0.0;
@@ -710,7 +718,7 @@ end;
 function DataPipeline.Transform(df: DataFrame): DataFrame;
 begin
   if not fitted then
-    raise new Exception('Pipeline is not fitted');
+    NotFittedError(ER_FIT_NOT_CALLED);NotFittedError(ER_FIT_NOT_CALLED);
 
   var current := df;
   foreach var step in steps do
diff --git a/bin/Lib/ValidationML.pas b/bin/Lib/ValidationML.pas
index fb7934164..5e5b10f83 100644
--- a/bin/Lib/ValidationML.pas
+++ b/bin/Lib/ValidationML.pas
@@ -76,6 +76,18 @@ const
   ER_DIM_MISMATCH_TRAIN_TEST =
     'Несоответствие размерностей в TrainTestSplit: X.RowCount={0}, y.Length={1}!!' +
     'Dimension mismatch in TrainTestSplit: X.RowCount={0}, y.Length={1}';
+  ER_TEST_RATIO_INVALID =
+    'Параметр testRatio должен быть в интервале (0,1), получено {0}!!' +
+    'Parameter testRatio must be in (0,1), got {0}';  
+  ER_K_INVALID =
+    'Некорректное значение k в KFold: k={0}, n={1}!!' +
+    'Invalid k in KFold: k={0}, n={1}';  
+  ER_K_INVALID_STRATIFIED =
+    'Некорректное значение k в StratifiedKFold: k={0}, n={1}!!' +
+    'Invalid k in StratifiedKFold: k={0}, n={1}';  
+  ER_STRATIFIED_LABELS_INVALID =
+    'StratifiedKFold поддерживает только метки 0/1!!' +
+    'StratifiedKFold supports only 0/1 labels';  
 
 //-----------------------------
 //         Validation
@@ -88,7 +100,7 @@ begin
     DimensionError(ER_DIM_MISMATCH_TRAIN_TEST, X.RowCount, y.Length);
 
   if (testRatio <= 0) or (testRatio >= 1) then
-    raise new Exception('testRatio must be in (0,1)');
+    ArgumentError(ER_TEST_RATIO_INVALID, testRatio);
 
   var n := X.RowCount;
   var p := X.ColCount;
@@ -137,7 +149,7 @@ static function Validation.KFold(n, k: integer; seed: integer):
   sequence of (array of integer, array of integer);
 begin
   if (k < 2) or (k > n) then
-    raise new Exception('Invalid k in KFold');
+    ArgumentError(ER_K_INVALID, k, n);
 
   var rnd := new System.Random(seed);
   var idx := (0..n-1).OrderBy(i -> rnd.Next).ToArray;
@@ -163,7 +175,7 @@ static function Validation.StratifiedKFold(y: Vector; k: integer;
 begin
   var n := y.Length;
   if (k < 2) or (k > n) then
-    raise new Exception('Invalid k in StratifiedKFold');
+    ArgumentError(ER_K_INVALID_STRATIFIED, k, n);
 
   var rnd := new System.Random(seed);
 
@@ -178,7 +190,7 @@ begin
     .ToArray;
 
   if idx0.Length + idx1.Length <> n then
-    raise new Exception('StratifiedKFold supports only 0/1 labels');
+    ArgumentError(ER_STRATIFIED_LABELS_INVALID);
 
   var base0 := idx0.Length div k;
   var extra0 := idx0.Length mod k;
@@ -213,7 +225,7 @@ static function Validation.CrossValidate(model: IModel; X: Matrix; y: Vector;
   k: integer; metric: (Vector,Vector) -> real; seed: integer): real;
 begin
   if X.RowCount <> y.Length then
-    raise new Exception('Dimension mismatch in CrossValidate');
+    DimensionError(ER_DIM_MISMATCH, X.RowCount, y.Length);
 
   var total := 0.0;
   var folds := 0;
@@ -260,7 +272,7 @@ static function Validation.StratifiedCrossValidate(
 ): real;
 begin
   if X.RowCount <> y.Length then
-    raise new Exception('Dimension mismatch in StratifiedCrossValidate');
+    DimensionError(ER_DIM_MISMATCH, X.RowCount, y.Length);
 
   var total := 0.0;
   var folds := 0;
@@ -314,7 +326,7 @@ class function GridSearch.Search<T>(
 ): (real, real, T); where T: IModel;
 begin
   if paramValues.Length = 0 then
-    raise new System.ArgumentException('paramValues is empty');
+    ArgumentError(ER_PARAM_VALUES_EMPTY);
 
   var bestParam := paramValues[0];
   var bestScore := -1e308;