Gradient Boosted Neural Network-Multi-Output or GBNN-MO model is a novel training procedure for shallow and deep neural networks. The GBNN-MO is specifically developed for single and multi-output regression problems. The GBNN-MO is developed over the GBNN model.
The focus of this project is to reproduce the two well-known multioutput regression models. The SST and ERC are introduced by [pyromitros-Xioufis, Eleftherios, et al. 2016]. The main work is presented in Java here.
The main idea of this simple repository is to build an ensemble of the Multi-layer perceptrons (classifier and regressor) with the AdaBoost boosting approach.
baseline for the "Multi-target regression via input space expansion: treating targets as inputs" (Spyromitros-Xioufis, Eleftherios, et al.) experiments. Having a baseline for the reference experiment brings more insight into the advantages of using output as an input in the model.
A gradient boosting classifier based on the tensorflow. A wrapper of the tf.BoostedTree of tensorflow based on the Sklearn standard.
Using this TOPSIS implementation is straightforward as importing it and writing only two lines. The important thing is the decision matrix in the type of pandas data frame.
ML Developer
Data scientist
Design, accuracy, innovation, and performance of the machine learning models matter. It establishes applicability, creates credibility, improves professionalism, and creates trust with the audience when done right.
Check out my latest ML-developed packages and projects.
My Repositories