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TL;DR
Yes, with overfitting all data becomes (non-linearly) separable (as long as the points don't precisely overlap).
Explanation
The problem with your argument is that you are using circles on a 2D plane, which is very difficult to learn. However, I think your argument can be made stronger with a decision-tree.
(0.2, 3.1)? --> yes -> star
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I am building on the first part of @Dylan's answer:
For general items like "dogs" pre-trained models are easily available. A good starting point is ImageNet. There are plenty of pre-trained models available for this dataset, e.g. see here for PyTorch. Since ImageNet includes multiple categories for a given item you can check this list to see which indexes ...
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Having consulted my professor, the person that wrote the question from the exercise book featured in the OP, here is their perspective:
Groups of data points can always be separated. The exception is when two points are at the same location.
However, the thing to consider is whether or not your decision boundary can separate unseen data, generated by the ...
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When I undersample and train my classifier on a balanced dataset and test on a balanced dataset, the results are pretty ok
It's not surprising that the results are good since the job is easier in this case. It's actually a mistake to test on the artificially balanced dataset, since it's not a fair evaluation of how the system will perform with real data.
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My two cents,
Evaluating results of a recommendation engine where test-set is unseen users only, will allow you doing exactly that.
Evaluating results on unseen users only.
If this is indeed the motivation(product/business wise) behind your recommendation engine I would suggest trying to tackle this problem directly.
If evaluating performance on 'unseen' ...
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I'm not sure that I understand every part of the process but there is one clear issue with it: because the CV is applied in the inner loop, there is a serious risk of overfitting the model with respect to the other parameters (feature subset, model type, sampling technique). Depending on the goal, this is not necessarily wrong but it's important to interpret ...
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There are some commands you must run, designed specifically for Colab:
from google.colab import drive
drive.mount('/content/drive')
With this, an authentication page will open. They want you to explicitly allow an access to your Drive. Click on the link and get a key code, that you can insert into an input line. At that point, you Colab Notebook is ready ...
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I wouldn't think so. If they're publishing their methodology, they want other people to see how well it works and apply it to their work. You'll probably want to explain why you think this method works best for your dataset and compare the performance results to other methods commonly used.
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One potential approach would have been to use a pretrained model to tag the photos you scraped to see if they contained a picture of a dog or not. Then just to keep things simple use that as a rough filter to see if the individual photo was suitable for your model.
If your task is highly specific it may be extremely difficult to find a pre trained image ...
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In simple terms: Linearly separable = a linear classifier could do the job. You could fit one straight line to correctly classify your data.
Technically, any problem can be broken down to a multitude of small linear decision surfaces; i.e. you approximate a non-linear function with a high number of small linear boundaries. That's what Neural Networks are ...
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