how to train model in this case

by Hao Yu   Last Updated August 10, 2018 13:19 PM

Assume that I trained a nonlinear model , one feature of the training data has very low variance, but the same feature of the test is quite different, at least in scale, from the one in the training data. Assume that this feature is really important to the prediction of the target. I found that in this case the model cannot learn the relation between the target and this feature, as the variance of this feature in the train set is very low. And for example , if the feature of the test set is 1/2 in scale compared to that in the train set, the model(like tree model) will predict the same value as for train set. My question is: how to train a model(linear or nonlinear) if the variance of one feature is very low and maybe very different in scale to the one in test set, but that feature is an important feature? thanks

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