diff --git a/public_api_v1_regression.py b/public_api_v1_regression.py
new file mode 100644
index 00000000..0e90eefd
--- /dev/null
+++ b/public_api_v1_regression.py
@@ -0,0 +1,51 @@
+from typing import Optional, Union, Self, Iterable, Tuple, Any, List
+
+import numpy as np
+from sklearn.linear_model import LinearRegression
+
+from mapie.regression import MapieRegressor
+from numpy.typing import ArrayLike, NDArray
+from sklearn.base import RegressorMixin
+from sklearn.model_selection import BaseCrossValidator
+
+from mapie.conformity_scores import BaseRegressionScore, AbsoluteConformityScore
+
+
+class NaiveConformalRegressor:
+    def __init__(
+        self,
+        estimator: RegressorMixin = LinearRegression,  # None doesn't exist anymore
+        conformity_score: BaseRegressionScore = AbsoluteConformityScore,  # Should we set this default?
+        alpha: Union[float, List[float]] = 0.9,  # Should we set this default? Actually an array is OK (already implemented, and avoid developing a less user-friendly reset_alpha method)
+        n_jobs: Optional[int] = None,
+        verbose: int = 0,
+        random_state: Optional[Union[int, np.random.RandomState]] = None,
+    ) -> None:
+        pass
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        # sample_weight: Optional[ArrayLike] = None, -> in fit_params
+        fit_params: dict,  # -> In __init__ ?
+        predict_params: dict,  # -> In __init__ ?
+    ) -> Self:
+        pass
+
+    def predict(
+        self,
+        X: ArrayLike,
+        optimize_beta: bool = False,  # Don't understand that one
+        allow_infinite_bounds: bool = False,
+        # **predict_params  -> To remove: redundant with predict_params in .fit()
+    ) -> Tuple[NDArray, NDArray]:
+        """
+        Returns
+        -------
+        Tuple[NDArray, NDArray]:
+          - the first element contains the point predictions, with shape (n_samples,)
+          - the second element contains the prediction intervals,
+            with shape (n_samples, 2) if alpha is a float, or (n_samples, 2, n_alpha) if alpha is an array of floats
+        """
+        pass