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1 vote

Which approach should I use to classify points above and below a sine function $y(x) = A + B \sin(Cx)$?

You can try using Fourier basis functions to transform your observable variables and then apply a general linear regression model. To clarify, if you have pairs of observables $(y_i, x_i)$ where $y_i$ ...
• 2,386
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Why are CNN binary classifier output probability distributions often skewed?

Yes, due to this issue, you should use temperature scaling after training your model. It will calibrate your probability and you will start to get the same kind of distributions. Here are a good ...
1 vote

So actually I managed to get hold of my lecturer to explain the argmax to argmin conversion. Generally speaking maximising $\frac{1}{||w||}$ is identical to minimising $||w||$. As $||w||$ in $\frac{1}{... 1 vote Does summing up word vectors destroy their meaning? But because the inputs have to have a fixed length Do they? Why? The go-to strategy would be to use an RNN (possibly with LSTM or GRUs, but probably not necessary) and train it to process input ... • 224 1 vote How to perform binary classification when one class is more predominant than the other? From your case, it seems like you want your algorithm to classify both 1s and 0s with high accuracy. To increase the number of 1s and get it to a comparable level as 0s, you could generate new ... • 11 1 vote When doing binary classification with neural networks, how can I order the importance of the features for a class? Two popular methods I’ve seen done: 1) For each feature, remove it and run the model and see the impact it has on the result. The idea is that the larger the impact, the more pertinent it was to ... • 2,359 1 vote Accepted Which loss function should I use for binary classification? There are several loss functions that you can use for binary classification. For example, you could use the binary cross-entropy or the hinge loss functions. See, for example, the tutorials Binary ... • 40.5k 1 vote Accepted How can I use Generative Adversarial Networks to solve the imbalanced class problem? In my experience, GANs work really well for the scenario of semi-supervised learning, where you don't necessarily have labels for all your class$B\$ data, but you do have a balanced dataset. In my (...
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