GRNN vs. GAM

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 similar patterns of functional relationships between model predictors and the response (red for GRNN and blue for GAM). However, while we have to determine the degree of freedom for each predictor in order to control the smoothness of a GAM model, there is only one tuning parameter governing the overall fitting of a GRNN model.

gam

Permutation Feature Importance (PFI) of GRNN

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 permuting values of the variable, which attempts to break the relationship between the predictor and the response.

Today, I added two functions to calculate PFI in the YAGeR project, e.g. the grnn.x_pfi() function (https://github.com/statcompute/yager/blob/master/code/grnn.x_pfi.R) calculating PFI of an individual variable and the grnn.pfi() function (https://github.com/statcompute/yager/blob/master/code/grnn.pfi.R) calculating PFI for all variables in the GRNN.

Below is an example showing how to use PFI to evaluate the variable importance. It turns out that the outcome looks very similar to the one created by the grnn.imp() function previously discussed.

pfi

Partial Dependence Plot (PDP) of GRNN

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:

  1. First trained a GRNN with the original training dataset
  2. Created an artificial dataset from the training data by keeping distinct values of the variable that we are interested in but replacing all values of other variables with their means. For instance, given a dataset with three variables X1, X2, and X3, if we are interested in the marginal effect of X1 with 3 distinct values, e.g. [X11 X12 X13], then the constructed dataset should look like {[X11 mean(X2) mean(X3)], [X12 mean(X2) mean(X3)], [X13 mean(X2) mean(X3)]}
  3. Calculated predicted values, namely [Pred1 Pred2 Pred3], based on the constructed dataset by using the GRNN created in the first step
  4. At last, the relationship between [X11 X12 X13] and [Pred1 Pred2 Pred3] is what we are looking for

The above-mentioned approach is computationally efficient but might be somewhat “brutal” in a sense that it doesn’t consider the variation in other variables.

By the end of Friday, my boss pointed me to a paper describing the partial dependence plot (Yes! In 53, we also have SVP who is technically savvy). The idea is very intriguing, albeit computationally expensive, and is delineated as below:

  1. First trained a GRNN with the original training dataset
  2. Based on the training dataset, get a list of distinct values from the variable of interest, e.g. [X11 X12 X13]. In this particular example, we created three separate datasets from the training data by keeping the other variables as they are but replacing all values of X1 with each of [X11 X12 X13] respectively
  3. With each of three constructed datasets above, calculated predicted values and then averaged them out such that we would have an average of predicted values for each of [X11 X12 X13], namely [Pavg1 Pavg2 Pavg3]
  4. The relationship between [X11 X12 X13] and [Pavg1 Pavg2 Pavg3] is the so-called Partial Dependence

The idea of PDP has been embedded in the YAGeR project (https://github.com/statcompute/yager/blob/master/code/grnn.partial.R). In the chart below, I compared outcomes of grnn.partial() and grnn.margin() side by side for two variables, e.g. the first not so predictive and the second very predictive. In this particular comparison, both appeared almost identical.

dpd

Merge MLP And CNN in Keras

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 the average of a multilayer perceptron network and a 1D convolutional network, just for fun and curiosity. Since the purpose of this exercise is to explore the network structure and the use case of Keras API, I didn’t bother to mess around with parameters.

model

Assess Variable Importance In GRNN

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 neural network. However, from the practical prospect, it is helpful to understand the individual contribution of each predictor to the overall goodness-of-fit of a GRNN. For instance, the variable importance can help us make up a beautiful business story to decorate our model. In addition, dropping variables with trivial contributions also helps us come up with a more parsimonious model as well as improve the computational efficiency.

In the YAGeR project (https://github.com/statcompute/yager), two functions have been added with the purpose to assess the variable importance in a GRNN. While the grnn.x_imp() function (https://github.com/statcompute/yager/blob/master/code/grnn.x_imp.R) will provide the importance assessment of a single variable, the grnn.imp() function (https://github.com/statcompute/yager/blob/master/code/grnn.imp.R) can give us a full picture of the variable importance for all variables in the GRNN. The returned value “imp1” is calculated as the decrease in AUC with all values for the variable of interest equal to its mean and the “imp2” is calculated as the decrease in AUC with the variable of interest dropped completely. The variable with a higher value of the decrease in AUC is deemed more important.

Below is an example demonstrating how to assess the variable importance in a GRNN. As shown in the output, there are three variables making no contribution to AUC statistic. It is also noted that dropping three unimportant variables in the GRNN can actually increase AUC in the hold-out sample. What’s more, marginal effects of variables remaining in the GRNN make more sense now with all showing nice monotonic relationships, in particular “tot_open_tr”.

imp

margin

Hyper-Parameter Optimization of General Regression Neural Networks

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 of the sigma by using various random number generators, including uniform random, Sobol sequence, and Latin hypercube sampling.

In addition to the random search, we can also directly optimize the sigma based on a pre-defined objective function by using the grnn.optmiz_auc() function (https://github.com/statcompute/yager/blob/master/code/grnn.optmiz_auc.R), in which either Golden section search by default or Brent’s method is employed in the one-dimension optimization. In the example below, the optimized sigma is able to yield a slightly higher AUC in both training and hold-out samples. As shown in the plot, the optimized sigma in red is right next to the best sigma in the random search.

Capture

Modeling Practices of Operational Losses in CCAR

Based upon the CCAR2019 benchmark report published by O.R.X, 88% participants in the survey that submitted results to the Fed used regression models to project operational losses, demonstrating a strong convergence. As described in the report, the OLS regression under the Gaussian distribution still seems the most prevalent approach.

Below is the summary of modeling approaches for operational losses based on my own experience and knowledge that might be helpful for other banking practitioners in CCAR.