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You might want to visit the definition of multi-class and multi-label problem. In this case looks like are aiming to solve a multi-label problem. Refer to the following problem, this might help you better understand your situation. https://stats.stackexchange.com/a/11866/286349


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Given data will keep increasing in the future along with the classes. You need to look at fast methods of doing things. Look at approximate KNN to do classification in high-volume data. Approximate KNN will solve your problem. Here's the library for it. Your accuracy will suffer a bit but this method would scale.


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The questions I ask myself when I see learning graphs are the following ones: Is the loss decreasing and the accuracy increasing ? if yes, your network is learning and everything works fine, which is already a good new. Have we reached a kind of plateau ? (in accuracy especially), which means learning is over. (Here maybe you could train your network a bit ...


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Try to give sklearn wrapper of xgboost to sklearn OneVsRestClassifier. https://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.sklearn If it doesn't work you can create four different label arrays in which samples belong to corresponding class labeled as 1s, and others as 0. Then training with each of the label arrays you can get ...


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There seems to be a confusion between multiclass and multilabel classification: Multiclass is the regular case where the task consists in predicting among N possible classes. For example an image can be either a dog or a horse or a cat, but always exactly one among these three animals. Multilabel is the when the task consists in predicting a set. For ...


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The answer depends on the library you are using. If you are using scikit - you wouldn't need to one hot encode the targets. Scikit handles it automatically. If you were using keras to build a neural network, you might want to use one hot encoded labels because the built in loss function in keras (e.g categorical crossentropy) expects labels to be one hot ...


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You could convert your categorical outputs back into their numeric equivalents, and then use the confusion matrix and normal


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Let us try to think from first principles for this problem. It is not clear what outcome you want to achieve by trying to predict cabin section here. I'm assuming it is purely learning to apply ML algorithms. We can hypothesize that the cabin sections assigned to a passengers would've been assigned by their class and fair paid. You can note (from the ...


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50% is quite decent because you have five labels and random guessing model would have achieved only 20% accuracy. So you know your model is learning something. The other thing you want to check out is whether this is suited to be a regression problem more than classification. For e.g, misclassifying a 5 (ground truth) into a 4 is better than misclassifying ...


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Actually, your objective here is not clear. Following your example, I belive you want to obtain likeness/relationship between users and places. So basically what you would want to do is to create a domain of users and places. Now for different addresses (eg. A and C), you can treat the same user (eg. One) as different users i.e. "One-A" and "...


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From the description this is not a multilabel problem because: Each of the three "classes" (columns) must have a label. In a multilabel problem every class is optional. Every "class" (column) appears to have a specific purpose subdivided into 4 labels. In a regular multilabel problem the labels are exchangeable, e.g. a document can have ...


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