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Welcome to the official documentation for GB-DNNR (Gradient Boosted - Deep Neural Network Regression), a powerful and flexible method for creating ensemble models in the realm of regression using deep neural networks. This wiki serves as a comprehensive guide to understanding, implementing, and customizing the GB-DNNR framework.
GB-DNNR is a Python library designed to facilitate the creation of gradient-boosted deep neural network regression models. all the classes, authored by Seyedsaman Emami and Gonzalo Martínez-Muñoz, provide a foundation for building robust regression models through the sequential addition of neural networks. Leveraging the power of gradient boosting, GB-DNNR enables users to construct complex regression models with superior predictive capabilities.