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You have to use a different set of specimens. Or you can keep one or two specimens from the original set aside and use them as test. Use data augmentation and transfer learning in that case.


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I have followed scikit-learn https://github.com/scikit-learn/scikit-learn/blob/fd237278e/sklearn/linear_model/_base.py#L293 link and was able to get predicted probabilities.


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Tree Based models like RF and XGBoost can handle the feature space created by either of the methods you suggested above. Additionally, you can try to do feature selection (use those statistical tests) to eliminate 0 variance features and then feed the filtered dataset to your models.


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SVC aims to find a separating hyperplane. That data you have is separable. Thus the hyperplane will go between x3 and x4. This are your support vectors. Now C is a parameter which allows you to make a trade off between errors allowed and the width of separating hyperplane. But your data does not need this trade off since it is already separable. Because of ...


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