Deep Learning with PyTorch

Handling missing values

Missing values are quite common in real-world machine learning problems. From our previous examples of predicting house prices, certain fields for the age of the house could be missing. It is often safe to replace the missing values with a number that may not occur otherwise. The algorithms will be able to identify the pattern. There are other techniques that are available to handle missing values that are more domain-specific.