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To answer your question, no you don't necessarily need only correlated features in your model, most models should be able to ignore those that are not correlating. Although having only one correlating feature is not optimal. To build a general model, you will usually include any features that may be helpful in other use cases, even if they are not always ...


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In the simplest case, doing regression with Transformers is just a matter of changing the loss function. BERT-like models that use the representation of the first technical token as an input to the classifier. You can replace the classifier with a regressor and pretty much nothing will change. The error from the regressor will get propagated to the rest of ...


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I understand you are looking for some interpretability. But if you recall Feature engineering, we mostly remove features which are of less value. What it means is that all the remaining features are contributing. What you may do a trade-off between Accuracy and Interpretability - Logistics Regression and Decision Tree will give you a clear picture on how ...


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It's because of the approach applied - CNN - We use 2-D Convolution on the image, Hence we need the image in 2-D. In this case, we use the fully connected neural network at the end. hence flattening is done at the end. CNN is used to reduce the dimension of the Image without losing the key information. A Simple neural network will become too big to train ...


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In the first example there are 60000 images of 2828 which is a 2d grayscale image. But in order to use CNN your images must be 3 dimensinal with height, width and channel as a new dimension. So you have to resize your every 28 * 28 image into 2828*1 image before you can send it into your CNN layers.


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