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As your context, You convert your audio clips to spectrogram images. The spectrogram is a visual representation of the spectrum of frequencies of sound. Now you need to Extract Features from your dataset. There are many feature extraction technique for audio data (See this blog for knowledge). The state of the art features extraction is now using: MFCC — ...


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The c (small one) term is bias or intercept added to the model. This is similar to intercept we add in case of linear regression. The library allows you to set bias term to zero too. When set to multinomial model, the cost function will try to minimize cross-entropy loss. Hard for me to write down the equation here, but I always go back to this useful ...


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Why is potentially hidden in your feature explanations. Given that your Features give some indication on why, like Patient demographics data, or some other Features that you can include here, you can use them to answer the why. Like this using shapley and or eli5 you can integrate it with your Standard predictor classes and for the explanations with user ...


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The error comes from attempting to fit a classifier (logistic regression) on a regression problem. If you are trying to predict prices (continuous outcome), you should use linear regression.


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