Category : multilabel-classification

in the classification problem in the following code the citation file is consist of two columns as the form when i run the following code class_values = sorted(papers["subject"].unique()) class_idx = {name: id for id, name in enumerate(class_values)} paper_idx = {name: idx for idx, name in enumerate(sorted(papers["paper_id"].unique()))} papers["paper_id"] = papers["paper_id"].apply(lambda name: paper_idx[name]) print(papers.sample(5)) citations["source"] = citations["source"].apply(lambda ..

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I am working on a multilabel classification problem. My dataset has 3 information about each photo which are "age, gender, ethnicity". To predict all three I used a multi-label classification. In this case, because the range of age is so big, the probabilities of each age’s becomes low like "maximum 0.1" while gender classes have ..

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I’m solving a multilabel classification problem with deep learning, but I have troubles with the loss function, as I can’t figure out how to determine "labels" and "logits". I have an y variable with 4 different labels (TV, Internet, Mobil, Fastnet) It works with other types of loss functions. Dataset –> https://www.transfernow.net/dl/20210930MS3TQBC1 This is my ..

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I’m trying to apply a multi-label classification. The shapes are: x_train.shape (3975, 3788) y_train.shape (3975, 66) x_test.shape (994, 3788) y_test.shape (994, 66) When I try to train, it gives the following error: ValueError: bad input shape (3975, 66) Any way to solve that? Here is the code: sgd = SGDClassifier() lr = LogisticRegression(solver=’lbfgs’) svc = ..

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