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I have an SFrame and a model:

train_data,test_data = products.random_split(.8, seed=0)
selected_words_model = graphlab.logistic_classifier.create(train_data,
                                                     target='sentiment',
                                                     features=selected_words,
                                                     validation_set=test_data)

After computing the accuracy of the model with `selected_words_model.evaluate(test_data) I'm asked "What is the accuracy majority class classifier on this task?" Yet I don't even know what this "means accuracy majority class classifier", shouldn't it be "accuracy of the majority class classifier" ?

Here is my attempt.

All these materials come from this coursera ML fundations course exercise.

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I suspect you are right that there is a missing "of the," and that the "majority class classifier" is the classifier that predicts the majority class for every input. Such a classifier is useful as a baseline model, and is particularly important when using accuracy as your metric. This matches what your notebook comments in the next bullet, so that's likely what was intended.

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  • $\begingroup$ Thank you ! That seems the most simple classification method ;) I still have one doube : how should I compare different learned models with the baseline approach where we are just predicting the majority class ? I tried a selected_words_model.show(view='Evaluation') but it still might depend on the threshold ? $\endgroup$ – Revolucion for Monica Sep 5 '19 at 14:33

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