Data Preprocessing Questions
Data imputation using variational autoencoders is a technique used in data preprocessing to fill in missing values in a dataset. Variational autoencoders (VAEs) are a type of neural network that can learn the underlying distribution of the input data. In the context of data imputation, VAEs are trained on the available data to learn the patterns and relationships within the dataset. Once trained, the VAE can generate plausible values for the missing data points based on the learned distribution. This imputation process helps to maintain the integrity and completeness of the dataset for further analysis or modeling.