satomi999

Thank you @pabloduque0 !! > Try altering the given priors for those two channels. You could try with the media priors but maybe the transformation priors as well. Sorry, this...

@pabloduque0 Thank you very much for your confirmation. Also, I have a total of 17 media variables. As mentioned above [here](https://github.com/google/lightweight_mmm/issues/77#:~:text=The%20error%20also%20occurred%20when%20I%20added%20either%20of%20those%20channels%20individually...), I excluded either one of the errored media from...

I use spend data. The following is the pre-processing and fitting part. Also, the fitting uses data for the entire period. ```python media_scaler = preprocessing.CustomScaler(divide_operation=jnp.mean) extra_features_scaler = preprocessing.CustomScaler(divide_operation=jnp.mean) target_scaler =...

train_s_sums is total spend per media. ```python spend_features = ["media_1", "media_2"] train_s_sums = df_train_s[spend_features].sum() ```