LinearRegression with fixed slope parameter I have some data $(x_{1},y_{1}), (x_{2},y_{2}), ..., (x_{n},y_{n})$, where both $x$ and $y$ represent real numbers (float). I want use Scikit-learns LinearRegression model to fit a model of the form:
$y_{i} = b_{0} + b_{1}x_i + e_{i} $
Typically, I know that OLS is used to compute the parameters $b_{0}, b_{1}$. However, in my case, I happen to know that  $b_{1}=c$ so I only want to fit $b_{0}$. Is there a way to force scikit-learn to use $b_{1}=c$ as the slope ratio and only estimate the intecept $b_0$, or is a custom class necessary?
You can just compute:  $\hat{b}_0 = \operatorname{mean}(y-cx)$