  In real-world applications, we can encounter situations when a well-trained
model has to be used to predict from a damaged dataset. The damage caused by
missing or corrupted values can be either on the level of individual instances
or on the level of entire features. Both situations have a negative impact on
the usability of the model on such a dataset. This paper focuses on the
scenario where entire features are missing which can be understood as a
specific case of transfer learning. Our aim is to experimentally research the
influence of various imputation methods on the performance of several
classification models. The imputation impact is researched on a combination of
traditional methods such as k-NN, linear regression, and MICE compared to
modern imputation methods such as multi-layer perceptron (MLP) and gradient
boosted trees (XGBT). For linear regression, MLP, and XGBT we also propose two
approaches to using them for multiple features imputation. The experiments were
performed on both real world and artificial datasets with continuous features
where different numbers of features, varying from one feature to 50%, were
missing. The results show that MICE and linear regression are generally good
imputers regardless of the conditions. On the other hand, the performance of
MLP and XGBT is strongly dataset dependent. Their performance is the best in
some cases, but more often they perform worse than MICE or linear regression.
