Re: Neural Network Modelling I would appreciate if someone could clarify the following points on using Early Stopping when fitting a Neural Network: Q1. How do we choose the value of parameter M to calculated the number of hidden units required (see course text at page 3-9 and example at page 3.14)? Does M not depend on the sample size n and the number of hidden unit h itself (i.e. M = n/h)? Q2. How should h be estimated if the target is multinomial or ordinal (see note at page 3.14)? Q3. Should we be concerned if the performance between Training and Validation show a significant divergence at iteration 0, like the chart on page 3.15, instead of the more regular pattern as shown on page 3.9? In other words, would that be an indication of possible issues with the initial parameter values? Q4. Am I right in saying that Early Stopping be used with both Multi Layer Perceptron and Radial Basis Functions networks? Q5. Can Early Stopping be used with Proc Neural as well? Would it be a matter of partitioning the data and then assessing the performance on Training and Validation datasets? (which would, of course, require some coding) ... View more

Re: Neural Network Modelling To have converged, should the 3 criteria reported on page 3.7 of the course text be all met at the same time or would it be enough for one of them to be met to declare converge of the algorithm? ... View more

Re: Predictive Modeling Using Logistic Regression Given that the parameter estimates with FAST Backward selection are only approximations of the regression coefficients, when would it be appropriate to use this method (see page 3-70 of course text)? Is the idea of using option FAST to just identify the most important predictors and then re-fit the model based on those inputs to get accurate estimates? ... View more

Re: Predictive Modeling Using Logistic Regression Just to confirm my understanding of the following statement at the bottom of page 3.47 of the course text: "Very liberal univariate screening might be helpful when the number of clusters created in PROC VARCLUS is still relatively large". Does "liberal univariate screening" mean that it is better to err on the side of allowing more inputs through the screening and then rely on regression selection techniques to find the best predictors? ... View more

Re: Predictive Modeling Using Logistic Regression Would it be possible to clarify why the presence of redundant inputs may increase the risk of overfitting (see page 3-34 of the course text)? ... View more

Re: Predictive Modeling Using Logistic Regression In cluster-mean imputation (page 3-11 and appendix B-7 of course text), should the variables used to define the clusters be restricted to those with missing values or could include all of them (i.e. with and without missing values)? Moreover, would it be possible to clarify statement at the bottom of page 3.11 of course text: “A simpler but sometimes useful alternative is to define a priori segments (for example, high, middle, low and unknown income) and then do mean or median imputation within each segment”. Not sure I understand the benefits of creating the above segments; however, I understand how the example shown on page 3.12 works: is the wording of page 3.11 correct? ... View more

Re: Neural Network Modeling Would it be possible to clarify how property "Use Inverse Priors" works in HP Neural Node? Does it mean the HP Neural Node does not make use of Prior Probabilities information defined for a data source? ... View more