First of all, thank you very much First of all, thank you very much. I have a project in which I am trying to examine the effect of fertility parameters on profitability in dairy cattle farms.

I have about 10 different criteria and these criteria are age at first calving, calving interval, lactation duration and so on. then we know the milk yield, calf yield and what the profit is due to the effect of these parameters.

What I am trying to do is this point. I want to find the effect of changes in the input parameters on the profit. So if the lactation period decreases by one day, what is the profit. I want to build a model where I can do this for the other 10 variables.
When I looked into it, I saw that I can use the hyper optimization model.
What other methods can I use. Which example can guide me.

Hi if you create a multiple linear regression model the coefficients would give you an indication of the effect one input parameter has
By Hyper Optimization you mean Hyperparameter optimization? If so that would be used in order to â€śtuneâ€ť a model to get the best kind of predictions. That is not what you search for (if I understand correclty)
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it is not normal values because i did not make survey and did not take data from program. i filled random but care about interval. yellow colones inputs ande 2 red colones outputs. For example M2 Cell value = 17 , if M2 cell =16 Colon , How can T2 ,U2 cell change. and other inputs
Thank You

Iâ€™ve taken a preliminary look at your data and have a number of questions. It seems to me that you canâ€™t optimize total yield directly since it depends on the number of cows. You should focus on average daily yield. Having said that the data doesnâ€™t make much sense, e.g. look at the following rows:
No Cows Ave. Yield Total Yield
1 173 38 117800
7 5 39 230958

I have a number of questions which can be found in this Excel workbook: Questions.xlsx (9.3 KB)
Finally, Iâ€™ve looked at the correlation between Ave. Yield and other factors and its very low.

You could use H2O AutoML, it will do the hyperparameter optimization for you, builds multiple models and does validation using crossvalidation. Hereâ€™s an example:

I donâ€™t think he needs (or wants) hyperparameter optimization. Heâ€™s after variable optimization/correlation (I think.) Its hard to tell and he hasnâ€™t responded to my questions. Reread his original post.

Take a look at these workflows. They should help you determine the most important regression coefficients. As @mlauber71 said if you want real optimization youâ€™ll probably have to employ some Python code. As @evert.homan_scilifelab.se commented you can try AutoML which can help find the best model. To use it effectively youâ€™ll need to play with setting hyperparameter bounds.

@hakanimamoglu if you want an optimization with constraints it will be good to have a real dataset if you are interested in problems of dairy farming indeed. This article seems to deal with such problems but does not provide data or code:

You can also try to employ ChatGPT though in the past I had problems to get it to write consistent code and samples for such problems. Maybe there are useful plugins now.

@hakanimamoglu if you want more regression machine learning examples I can offer this collection but we mentioned depending on your task it might not give you the answer you are looking for.