multilabel-classification

Xg boost for multilabel classification?

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问题 Is it possible to use xgboost for multilabel classification? Now I use OneVsRestClassifier over GradientBoostingClassifier from sklearn. It works, but use only one core from my CPU. In my data I have ~45 features and the task is to predict about 20 columns with binary(boolean) data. Metric is mean average precision (map@7). If you have a short example of code to share, that would be great. 回答1: There are a couple of ways to do that, one of which is the one you already suggested: 1. from

How does Keras handle multilabel classification?

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I am unsure how to interpret the default behavior of Keras in the following situation: My Y (ground truth) was set up using scikit-learn's MultilabelBinarizer (). Therefore, to give a random example, one row of my y column is one-hot encoded as such: [0,0,0,1,0,1,0,0,0,0,1] . So I have 11 classes that could be predicted, and more than one can be true; hence the multilabel nature of the problem. There are three labels for this particular sample. I train the model as I would for a non multilabel problem (business as usual) and I get no errors. from keras.models import Sequential from keras

How does Keras handle multilabel classification?

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问题 I am unsure how to interpret the default behavior of Keras in the following situation: My Y (ground truth) was set up using scikit-learn's MultilabelBinarizer (). Therefore, to give a random example, one row of my y column is one-hot encoded as such: [0,0,0,1,0,1,0,0,0,0,1] . So I have 11 classes that could be predicted, and more than one can be true; hence the multilabel nature of the problem. There are three labels for this particular sample. I train the model as I would for a non