# I have an problem, related to machine learning and which method is used to proceed further?

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naresh bhimchand il 26 Set 2022
Risposto: Yash Sharma il 7 Set 2023
Hi all,
I have an data with 11 rows and 2 columns in which the single output is the combination of single row and column. As an probability i will get 11x11 outputs.And i need to find the best solution. from the above problem 11x11, i need to find a 1x1 single value as an best solution.
for the above problem, which type of method will be better to achieve the best solution to proceed further.And if possible, please share the link of some examples related to my problem.
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Rahul il 11 Ott 2022
Can you please elaborate your problem? What exactly you want to execute? There are various machine learning algorithms for classification. Please share some details about the dataset, the target variable, etc.
John D'Errico il 25 Ott 2022
So just evaluate the function at all 121 combinatinos of values. Then choose the one that makes you happy. Simple loops would suffice, but you can use meshgrid to do some of the work for you.

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### Risposte (1)

Yash Sharma il 7 Set 2023
I understand that you have a 11x11 matrix and you want to select 1x1 element as the best solution.
One possible approach to find the best solution is to use optimization techniques. For now, you can consider treating the 11x11 output grid as an objective function and use an optimization algorithm to find the maximum or minimum value.
MATLAB provides various optimization functions and tools like “fminsearch”,fminunc”,fmincon”, “ga” that you can utilize for this purpose. The choice of the specific optimization algorithm depends on the nature of your problem, constraints, and specific requirements.
Another possible approach is to use Machine Learning algorithms to find the best solution from 11x11 grid, you can Treat the problem as a regression task, where you train a model to predict the single value output based on the 11x11 input grid. You can use algorithms such as linear regression, decision trees, random forests, or gradient boosting.
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