TabNet is a neural network architecture proposed by Google AI that is referred to as a deep learning model. The purpose of this model is to quickly process large datasets and efficiently generate real-time predictions.
TabNet is based on a combination of recent advances in deep learning, namely attention mechanisms and autoML. The design of TabNet is tailored for tabular data such as that found in database tables, or CSV files.
The TabNet model consists of several parts, including a graph lattice, feature selector, attention gate, and forecasting network. The graph lattice is an encoding used to capture the relationships between the variables and their respective values. The feature selector is used to select which of the variables in the input data has the highest influence on the output. The attention gate uses the selected features to determine when to pass certain variables to the forecasting network. Lastly, the forecasting network is used to generate predictions.
The primary advantage of TabNet is its ability to generate real-time predictions on large datasets that are both accurate and efficient. Additionally, TabNet allows the user to select the most important features in the data and focus their attention on them. This helps to reduce the complexity of the entire model and the computational expenses of using it.
TabNet has shown great promise in recent research and has proven to be a great alternative to existing deep learning models on large data sets. TabNet is a powerful tool for a wide variety of applications including time series analysis, medical diagnosis, fraud detection, and many more.