Data Preprocessing Questions
Data imputation using autoencoders is a technique used in data preprocessing to fill in missing values in a dataset. Autoencoders are a type of neural network that consists of an encoder and a decoder. The encoder compresses the input data into a lower-dimensional representation, while the decoder reconstructs the original input from this representation.
In the context of data imputation, autoencoders can be trained on the available data with missing values. The encoder learns to capture the underlying patterns and relationships in the data, while the decoder learns to reconstruct the missing values based on this learned representation. Once the autoencoder is trained, it can be used to predict and fill in the missing values in new or unseen data.
By utilizing autoencoders for data imputation, it is possible to impute missing values based on the learned patterns and relationships in the data, which can help preserve the integrity and quality of the dataset for further analysis or modeling tasks.