diff --git a/CHANGELOG.md b/CHANGELOG.md
index 9b6b2805e..48ea5f6cc 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -28,7 +28,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 - Allow `RNNModel` to work with categorical features ([#334](https://github.com/etna-team/etna/pull/334))
 - Allow `DeepARNativeModel` and `MLPModel` to work with categorical features ([#336](https://github.com/etna-team/etna/pull/336))
 - Allow `DeepState` to work with categorical features ([#342](https://github.com/etna-team/etna/pull/342))
-- 
+- Allow encoders to return numeric features ([#352](https://github.com/etna-team/etna/pull/352))
 - 
 - 
 - 
diff --git a/etna/transforms/encoders/categorical.py b/etna/transforms/encoders/categorical.py
index 135d3ce24..8926ad7a2 100644
--- a/etna/transforms/encoders/categorical.py
+++ b/etna/transforms/encoders/categorical.py
@@ -8,6 +8,7 @@
 from sklearn import preprocessing
 from sklearn.utils._encode import _check_unknown
 from sklearn.utils._encode import _encode
+from typing_extensions import assert_never
 
 from etna.datasets import TSDataset
 from etna.distributions import BaseDistribution
@@ -22,9 +23,26 @@ class ImputerMode(str, Enum):
     mean = "mean"
     none = "none"
 
+    @classmethod
+    def _missing_(cls, value):
+        raise ValueError(f"The strategy '{value}' doesn't exist")
+
+
+class ReturnType(str, Enum):
+    """Enum for data types of returned columns."""
+
+    categorical = "categorical"
+    numeric = "numeric"
+
+    @classmethod
+    def _missing_(cls, value):
+        raise NotImplementedError(
+            f"{value} is not a valid {cls.__name__}. Supported types: {', '.join([repr(m.value) for m in cls])}"
+        )
+
 
 class _LabelEncoder(preprocessing.LabelEncoder):
-    def transform(self, y: pd.Series, strategy: str):
+    def transform(self, y: pd.Series, strategy: ImputerMode):
         diff = _check_unknown(y, known_values=self.classes_)
 
         is_new_index = np.isin(y, diff)
@@ -34,14 +52,14 @@ def transform(self, y: pd.Series, strategy: str):
             float
         )
 
-        if strategy == ImputerMode.none:
+        if strategy is ImputerMode.none:
             filling_value = None
-        elif strategy == ImputerMode.new_value:
+        elif strategy is ImputerMode.new_value:
             filling_value = -1
-        elif strategy == ImputerMode.mean:
+        elif strategy is ImputerMode.mean:
             filling_value = np.mean(encoded[~np.isin(y, diff)])
         else:
-            raise ValueError(f"The strategy '{strategy}' doesn't exist")
+            assert_never(strategy)
 
         encoded[is_new_index] = filling_value
         return encoded
@@ -50,7 +68,13 @@ def transform(self, y: pd.Series, strategy: str):
 class LabelEncoderTransform(IrreversibleTransform):
     """Encode categorical feature with value between 0 and n_classes-1."""
 
-    def __init__(self, in_column: str, out_column: Optional[str] = None, strategy: str = ImputerMode.mean):
+    def __init__(
+        self,
+        in_column: str,
+        out_column: Optional[str] = None,
+        strategy: str = ImputerMode.mean,
+        return_type: str = ReturnType.categorical,
+    ):
         """
         Init LabelEncoderTransform.
 
@@ -68,12 +92,19 @@ def __init__(self, in_column: str, out_column: Optional[str] = None, strategy: s
             - If "mean", then replace missing values using the mean in encoded column
 
             - If "none", then replace missing values with None
+        return_type:
+            Data type of returned columns:
+
+            - If "categorical", then returned columns will have "category" data type
+
+            - If "numeric", then returned columns will have float data type
 
         """
         super().__init__(required_features=[in_column])
         self.in_column = in_column
         self.out_column = out_column
-        self.strategy = strategy
+        self.strategy = ImputerMode(strategy)
+        self.return_type = ReturnType(return_type)
         self.le = _LabelEncoder()
         self.in_column_regressor: Optional[bool] = None
 
@@ -123,7 +154,15 @@ def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
         out_column = self._get_column_name()
         result_df = TSDataset.to_flatten(df)
         result_df[out_column] = self.le.transform(result_df[self.in_column], self.strategy)
-        result_df[out_column] = result_df[out_column].astype("category")
+
+        if self.return_type is ReturnType.categorical:
+            return_type = "category"
+        elif self.return_type is ReturnType.numeric:
+            return_type = "float"
+        else:
+            assert_never(self.return_type)
+
+        result_df[out_column] = result_df[out_column].astype(return_type)
         result_df = TSDataset.to_dataset(result_df)
         return result_df
 
@@ -155,7 +194,7 @@ class OneHotEncoderTransform(IrreversibleTransform):
     encoded columns for this feature will be all zeros.
     """
 
-    def __init__(self, in_column: str, out_column: Optional[str] = None):
+    def __init__(self, in_column: str, out_column: Optional[str] = None, return_type: str = ReturnType.categorical):
         """
         Init OneHotEncoderTransform.
 
@@ -165,10 +204,17 @@ def __init__(self, in_column: str, out_column: Optional[str] = None):
             Name of column to be encoded
         out_column:
             Prefix of names of added columns. If not given, use ``self.__repr__()``
+        return_type:
+            Data type of returned columns:
+
+            - If "categorical", then returned columns will have "category" data type
+
+            - If "numeric", then returned columns will have float data type
         """
         super().__init__(required_features=[in_column])
         self.in_column = in_column
         self.out_column = out_column
+        self.return_type = ReturnType(return_type)
         self.ohe = preprocessing.OneHotEncoder(handle_unknown="ignore", sparse=False, dtype=int)
         self.in_column_regressor: Optional[bool] = None
 
@@ -219,7 +265,13 @@ def _transform(self, df: pd.DataFrame) -> pd.DataFrame:
         x = result_df[[self.in_column]]
         out_columns = self._get_out_column_names()
         result_df[out_columns] = self.ohe.transform(X=x)
-        result_df[out_columns] = result_df[out_columns].astype("category")
+
+        if self.return_type == ReturnType.categorical:
+            return_type = "category"
+        elif self.return_type == ReturnType.numeric:
+            return_type = "float"
+
+        result_df[out_columns] = result_df[out_columns].astype(return_type)
         result_df = TSDataset.to_dataset(result_df)
         return result_df
 
diff --git a/tests/test_transforms/test_encoders/test_categorical_transform.py b/tests/test_transforms/test_encoders/test_categorical_transform.py
index d0a047147..6e866c737 100644
--- a/tests/test_transforms/test_encoders/test_categorical_transform.py
+++ b/tests/test_transforms/test_encoders/test_categorical_transform.py
@@ -152,6 +152,24 @@ def test_label_encoder_simple(dtype):
         assert df_transformed.equals(df_expected)
 
 
+@pytest.mark.parametrize("return_type, expected_type", [("categorical", "category"), ("numeric", "float64")])
+def test_label_encoder_return_type(return_type, expected_type):
+    """Test that LabelEncoderTransform return column with correct dtype."""
+    ts, _ = get_ts_for_label_encoding()
+    le = LabelEncoderTransform(in_column=f"regressor_0", out_column="test", return_type=return_type)
+    le.fit(ts)
+    df_transformed = le.transform(deepcopy(ts)).to_pandas()["segment_0"]["test"]
+    type_transformed = df_transformed.dtypes.name
+    assert type_transformed == expected_type
+
+
+@pytest.mark.parametrize("return_type", ["int", "all"])
+def test_wrong_mode_type(return_type):
+    """Check that Exception raises when passed wrong return_type"""
+    with pytest.raises(NotImplementedError, match=f"{return_type} is not a valid ImputerType."):
+        _ = LabelEncoderTransform(in_column=f"regressor_0", out_column="test", return_type=return_type)
+
+
 @pytest.mark.parametrize("dtype", ["float", "int", "str", "category"])
 def test_ohe_encoder_simple(dtype):
     """Test that OneHotEncoderTransform works correct in a simple case."""
@@ -165,13 +183,31 @@ def test_ohe_encoder_simple(dtype):
         assert df_transformed.equals(df_expected)
 
 
+@pytest.mark.parametrize("return_type, expected_type", [("categorical", "category"), ("numeric", "float64")])
+def test_ohe_encoder_return_type(return_type, expected_type):
+    """Test that OneHotEncoderTransform return columns with correct dtype."""
+    ts, _ = get_ts_for_label_encoding()
+    ohe = OneHotEncoderTransform(in_column=f"regressor_0", out_column="test", return_type=return_type)
+    ohe.fit(ts)
+    cols = ohe._get_out_column_names()
+    df_transformed = ohe.transform(deepcopy(ts)).to_pandas()["segment_0"][cols]
+    for col in df_transformed.columns:
+        type_transformed = df_transformed[col].dtypes.name
+        assert type_transformed == expected_type
+
+
+@pytest.mark.parametrize("return_type", ["int", "all"])
+def test_wrong_mode_type(return_type):
+    """Check that Exception raises when passed wrong return_type"""
+    with pytest.raises(NotImplementedError, match=f"{return_type} is not a valid ReturnType."):
+        _ = OneHotEncoderTransform(in_column=f"regressor_0", out_column="test", return_type=return_type)
+
+
 def test_value_error_label_encoder(ts_for_label_encoding):
     """Test LabelEncoderTransform with wrong strategy."""
     ts, _ = ts_for_label_encoding
-    with pytest.raises(ValueError, match="The strategy"):
+    with pytest.raises(ValueError, match="The strategy 'fake_strategy' doesn't exist"):
         le = LabelEncoderTransform(in_column="target", strategy="fake_strategy")
-        le.fit(ts)
-        le.transform(ts)
 
 
 @pytest.mark.parametrize(