Hi Eugen, You should be able to do this without the LinearRegression API. I believe for a linear regression model ( https://en.wikipedia.org/wiki/Simple_linear_regression) [image: image.png] the best estimator for the intercept will be [image: image.png] where \overline{y} is the average of the target variable and \overline{x} is the average of the features in your new training data. \hat{\beta} will remain your previously fit slope. This lets you fit a new intercept (\alpha) given the previous slope (\beta) and the averages of your new training data, which you should be able to compute in a straightforward manner using Spark.
I imagine there might be a way to do this with the LinearRegression API though. Thanks, Steven On Sat, Mar 21, 2020 at 12:49 AM <eugen.wintersber...@gmail.com> wrote: > Hi, > I was wondering if it would be possible to fit only the intercept on a > LinearRegression instance by providing a known coefficient? > > Here is some background information: we have a problem where linear > regression is well suited as a predictor. However, the model requires > continuous adoption. During an initial training, the coefficient and the > intercept of the linear model are determined from a given set of training > data. Later, this model requires adoption during which the intercept has to > be adopted to a new set of training data (the coefficient, in other words > the slope, remains the same as obtained from the initial model). > > I had a look on the Java API for LinearRegression and could not find a way > how to only fit the intercept and set initial parameters for a fit. Am I > missing something? Is there a way how to to do this with the > LinearRegression class in Sparks' ML package or do I have to use a > different approach? > > Thanks in advance. > > regards > Eugen > >
