How to use Machine Learning Algorithms in classification for categorical problem?
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Mohamed
le 16 Nov 2023
Commenté : Image Analyst
le 18 Nov 2023
I have a matrix with 100*100 data points. I need to apply ML for classification of (Yes, there is an event to be 1, or No, there is no event 0). In addirion, I should only label 7500 (as 1 or 0) (75%) for training and no adding 1 or 0 for the remainder 2500 (25%) for testing?
Which models I should try? If I need to do comparative study, which algorithms I should try?
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the cyclist
le 17 Nov 2023
This is helpful information, but it is still not clear how to make this into a classification problem. Let's modify my small example:
M = [10 20 30 40 50;
20 35 45 55 60;
25 40 60 75 65;
25 20 30 40 50;
20 5 15 35 45];
There are two points that are "local minimum" points: The value 10 at location (1,1), and the value 5 at location (5,2).
I also have a local maximum: the value 75 at location (3,4).
Is the first step to find the local minima? (That is not a machine learning problem.)
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Image Analyst
le 17 Nov 2023
In the 100x100 matrix, is one of the columns the response (ground truth or known true value), and the other 99 columns are the predictors? Or do you have another vector for the response values?
Have you tried the Classification Learner app on the Apps tab of the tool ribbon? You should. You can then try a wide variety of different classification methods and pick the best one and export code for it.
If you have any more questions, then attach your data and code to read it in with the paperclip icon after you read this:
2 commentaires
Image Analyst
le 18 Nov 2023
I agree with @the cyclist - you just need to examine the image to find the peak(s), and the object(s) will be at the peak location.
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the cyclist
le 17 Nov 2023
Based on your replies to my comments, this does not seem like a machine learning classification problem to me. It seems like a peak-finding problem.
Take a look at this question/answer from the MathWorks support team, about 2-dimensional peak-finding. Maybe it will help you.
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