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2

These are several things you can try: Use quartic error, $(y - \hat{y})^4$, instead of quadratic error. This is going to penalize a lot big errors, way more than MSE. The issue is that this is not implemented in xgboost, and you would need to develop a custom loss. If your target is always positive, you can use the target as training weights. This will give ...


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I think your confusion comes from the fact that the false negative of $M$ as you thought it, is not top-$k$ accuracy but perhaps $1$ $-$ top-$k$. Moreover, how do you evaluate the condition if g' in top-$k$ most similarity graphs with g? In any case, for your case I thought the following. Let's say that if the model $M$ returns a value $o > o^*$, where $o,...


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The only reason you keep different set it to test the model on unseen data. Seeing is not just about using the data in training but also when you use it for testing and tweak your parm. In that hit-and-trial, you are actually fitting your model to the test data. Crux is - The last set must be treated as new data and tried only a few times


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What is that equation and is it standardly used? I havn't seen exactly that before, so I guess it's not "standard", but it's not so unusual. The equation is "just" 1 - (normal distribution). If you think of the normal distribution as a measure of "how similar a sample is to the mean", then that equation turns the similarity (...


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You don't always need 3 separate datasets. You usually split a dataset into 3 if you are doing some parameter or hyperparameter tuning before choosing a final model. Tuning will usually add bias from the 2nd dataset into your model, decreasing it's performance. For instance: If you are manually tuning a model over several iterations and using the results ...


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If you just want to see how similar the clustering is between 2 algorithms, using the sklearn.metrics.adjusted_rand_score() function is a good starting point. This will work for unsupervised learning, no need for a label. https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_rand_score.html Or are you looking at choosing the best ...


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Accuracy has a specific meaning classification - the data points with predicted labels must exactly match actual labels over the total number of data points. In order to calculate accuracy, you need the actual labels for each data point. If you do not have actual labels for a data point, those data points can not be used in the analysis.


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