Machine learning with incomplete data
<p>I have one millions sample and there are about 1000 features. However, only a subset of features are measured for each sample. I want to perform machine learning to predict the result based on the features, however, I do not know how to handle the missing data. Since data are missing in random order, I cannot classify data based on the missing feature because the number of classes would be huge and there would be only few samples in each class. What is the best solution for handling this kind of problem?</p>