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edited Jul 25, 2020 at 12:29

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How can I predict the true label for data with incomplete features based on the trained model learned bywith data with completemore features?

for example, theSuppose I have a model that was learned by training datatrained with completea dataset that contains the features (f1,f2,f3,f4(f1, f2, f3, f4, f5, f6). However,f5 my test dataset does not contain all features of the training dataset,f6)

but, only (f1, f2, f3). How can I wonderpredict the model can test data with incomplete features (f1,f2,f3) to attach true label into theseof the entries of this test dataset

I am waiting for ML specialist's answer

Thank you so much !! without all features?

How can I predict the true label for data with incomplete features based on the model learned by data with complete features?

for example, the model was learned by training data with complete features (f1,f2,f3,f4,f5,f6)

but, I wonder the model can test data with incomplete features (f1,f2,f3) to attach true label into these test dataset

I am waiting for ML specialist's answer

Thank you so much !!

How can I predict the true label for data with incomplete features based on the trained model with data with more features?

Suppose I have a model that was trained with a dataset that contains the features (f1, f2, f3, f4, f5, f6). However, my test dataset does not contain all features of the training dataset, but only (f1, f2, f3). How can I predict the true label of the entries of this test dataset without all features?