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random forest's feature importances are not reliable and you should probably avoid them. Instead you can use permutation_importance: https://scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py


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I would classify each pixel separately instead of giving a label to the whole image. Sadly preparing the training data is very tedious and time-consuming. Let's say the input image has dimensions of 200 x 300 x 3 (RGB) and there are two classes of regions you want to identify. A few approaches come to mind: 1) Train two separate networks, each forecasting ...


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I think what you are actually talking about is semantic segmentation (where you label pixels individually). There is a difference in theses tasks like Classification, Detection or Semantic Segmentation. Classification refers to the task of giving a (usually) single label to the whole image, e.g. cat. But as you already noticed this does not nececerraly ...


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You can't label things you don't know. The goal of labeling is to label the things you want the classifier to learn so that when you run it in inference mode you can discover what is in your data (new data that you didn't use for training, validating, or testing). It is not a good idea to label small objects like the 'blue water' unless it is important to ...


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To my knowledge the deployment model (that you will test on underwater images) as inference will not have a negative effect. Yet drawings may even help differentiate some classes at training and inference. Provided that you won't use drawings in inference, adding them in training phase will not necessarily hurt the accuracy. Note that a drawing of a ...


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First of all, there are multiple factors on how well models will work. Amount of data, source of data, hyperparameters, model type, training time etc... All of these will affect the accuracy. However, no classifier will work best in general. It all depends on the different factors, and not one can satisfy all, at least for now. For improving the accuracy, ...


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The accuracy depends on various factors. Might not always be the algorithm. For example a cleaner data with a poor algorithm might still give better results and vice versa. What are the preprocessing techniques you are using? This preprocessing techniques article is a good starting point for html data. And by vectorising I assume you mean word2vec, use a ...


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I think if you got the dataset, then a standard 1d convolutional neural network would work to some extent. It's not that there is some property of nearby sounds that it would pick up on. It would just memorize all the sounds that tend to come from your desk. I think the coding part would be pretty standard stuff. But collecting the data will be hard. You ...


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My suggestion is to go with 1st option. reason is you will get to know much about data and initial stage will find some challenges in developing the model, over a period of time you will get to better results after hypertunning. Please go through article , ignore you have already read this article


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Couldn't find the specific India dataset for traffic signs, but here is the generalized one from the Open Image Dataset by Google link Maybe, you can classify them based on the Indian standards, or there may be an option to request images that were tagged in India (location)


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One more idea - I recall learning about Neyman-Pearson task in my studies. It is a statistical learning method for binary classification problem where overlooked danger (false negative error) is much unwanted. You set a desired threshold for false negative error rate and then minimize false positive error. You just need to measure conditional probabilities ...


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