diff --git a/freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py b/freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py
index f2d7ff370..15c8db256 100644
--- a/freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py
+++ b/freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py
@@ -6,26 +6,21 @@ from sklearn.utils.validation import has_fit_parameter, validate_data
 class FreqaiMultiOutputRegressor(MultiOutputRegressor):
     def fit(self, X, y, sample_weight=None, fit_params=None):
         """Fit the model to data, separately for each output variable.
-        Parameters
-        ----------
-        X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        :param X: {array-like, sparse matrix} of shape (n_samples, n_features)
             The input data.
-        y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
+        :param y: {array-like, sparse matrix} of shape (n_samples, n_outputs)
             Multi-output targets. An indicator matrix turns on multilabel
             estimation.
-        sample_weight : array-like of shape (n_samples,), default=None
+        :param sample_weight: array-like of shape (n_samples,), default=None
             Sample weights. If `None`, then samples are equally weighted.
             Only supported if the underlying regressor supports sample
             weights.
-        fit_params : A list of dicts for the fit_params
+
+        :param fit_params: A list of dicts for the fit_params
             Parameters passed to the ``estimator.fit`` method of each step.
             Each dict may contain same or different values (e.g. different
             eval_sets or init_models)
-            .. versionadded:: 0.23
-        Returns
-        -------
-        self : object
-            Returns a fitted instance.
+
         """
 
         if not hasattr(self.estimator, "fit"):