only transforming the target to 2d, if there is one, and if it is 1d
@@ -303,6 +303,7 @@ class EEGRegressor(NeuralNetRegressor):
)
def fit(self, X, y, **kwargs):
+ if y is not None:
if y.ndim == 1:
y = np.array(y).reshape(-1, 1)
super().fit(X=X, y=y, **kwargs)
