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The question is actually about understanding what it means to "take imbalance into account": Micro-average "takes imbalance into account" in the sense that the resulting performance is based on the proportion of every class, i.e. the performance of a large class has more impact on the result than of a small class. Macro-average "...


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Movie 2 get second best score but it has not been rated by user although it's highly relevant for user A based on other user ratings. The issue with this is that you're injecting your opinion into what items are relevant to user A. How NDCG@3 should be calculated in this example ? You cannot evaluate your model on items the user has not rated. Generally ...


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It's better to use the F-score(F-measure) in this case to evaluate youre model. To calculate the F-score you can use the following equation: $\textrm{ F score} = \frac{(2 * Precision * Recall) }{ (Precision + Recall)} = \frac{tp}{tp+\frac{1}{2}(fp+fn)}$ where: tp : True Positives fp : False Positives fn : False Negatives Dont use accuracy as you may ...


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does it make sense to use micro and macro precision in binary classification problems when classes are imbalanced? In general micro- and macro-average performance are not relevant in binary classification, whether the classes are balanced or not. Their value can be especially misleading if there is a strong imbalance, because it takes into account both the ...


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First off, in terms of how to make a train/validation/test split, I would only include users with 2 or more interactions, and for each user, remove the latest interaction. Use this latest interaction as a target and recommend, for each user, their top streamers. You then compare your recommendations with the target, and compute a performance metric. ...


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This is what I see using data from Pascal VOC that was converted using this example script: I am using Tensorflow 2. I think it's just the images preprocessed using the data_augmentation_options that were specified in your pipeline.config file. I seem to recall seeing bounding boxes on the images in Tensorboard back when I was using the Object Detection API ...


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You gave an exemple of a value function, but not an optimal value function, that would verify : $$ V^{*}(s) =max_\pi(V^{\pi}(s)) $$ Such a value function would allow to use one-step look ahead approach to get the optimal policy. Because the optimal value function would be defined somehow iteratively to take neighbor value into account. In other words, the ...


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First, it makes sense that adding features will improve your performance, just make sure you do evaluation carefully and not overuse the same validation dataset (and if yes try to re evaluate it on an unseen independent different test set) to ensure you are not overfitted. After that, you can use Shapley values in its aggregate look to see which features (or ...


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