In practice, GRNN is very similar to GAM (Generalized Additive Models) in the sense that they both shared the flexibility of approximating non-linear functions. In the example below, both GRNN and GAM were applied to the Kyphosis data that has been widely experimented in examples of GAM and revealed very ...

In the post https://statcompute.wordpress.com/2019/10/13/assess-variable-importance-in-grnn, it was shown how to assess the variable importance of a GRNN by the decrease in GoF statistics, e.g. AUC, after averaging or dropping the variable of interest. The permutation feature importance evaluates the variable importance in a similar manner by ...

The function grnn.margin() (https://github.com/statcompute/yager/blob/master/code/grnn.margin.R) was my first attempt to explore the relationship between each predictor and the response in a General Regression Neural Network, which usually is considered the Black-Box model. The idea is described below: First trained a ...

In the post (https://statcompute.wordpress.com/2017/01/08/an-example-of-merge-layer-in-keras), it was shown how to build a merge-layer DNN by using the Keras Sequential model. In the example below, I tried to scratch a merge-layer DNN with the Keras functional API in both R and Python. In particular, the merge-layer DNN is ...

Technically speaking, there is no need to evaluate the variable importance and to perform the variable selection in the training of a GRNN. It’s also been a consensus that the neural network is a black-box model and it is not an easy task to assess the variable importance in ...

A major advantage of General Regression Neural Networks (GRNN) over other types of neural networks is that there is only a single hyper-parameter, namely the sigma. In the previous post (https://statcompute.wordpress.com/2019/07/06/latin-hypercube-sampling-in-hyper-parameter-optimization), I’ve shown how to use the random search strategy to find a close-to-optimal value ...

It has been mentioned in https://github.com/statcompute/GRnnet that GRNN is an ideal approach employed to develop performance benchmarks for a variety of risk models. People might wonder what the purpose of performance benchmarks is and why we would even need one at all. Sometimes, a model developer ...

In my previous post https://statcompute.wordpress.com/2019/02/03/sobol-sequence-vs-uniform-random-in-hyper-parameter-optimization/, I’ve shown the difference between the uniform pseudo random and the quasi random number generators in the hyper-parameter optimization of machine learning. Latin Hypercube Sampling (LHS) is another interesting way to generate near-random sequences with a very simple idea. Let’...

In the post https://statcompute.wordpress.com/2019/04/27/more-general-weighted-binning, I’ve shown how to do the weighted binning with the function wqtl_bin() by the iterative partitioning. However, the outcome from wqtl_bin() sometimes can be too coarse. The function wgbm_bin() (https://github.com/statcompute/MonotonicBinning/blob/master/code/wgbm_...