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I've searched exhaustively on this forum and elsewhere, and have come across a lot of great material. However, I'm ultimately still confused. Here's a basic, concrete example of what I'd like to accomplish, my approach for doing so, and my questions.

I have a dataset sized 1000 x 51; 1000 observations, each with 50 numeric features and 1 binary response variable marked with either "0" or "1". "0" indicates a response of "not-early," and "1" indicates a response of "early." I'd like to build a single LASSO logistic regression model to predict, on a testing set that does not include response variables, whether each testing observations result in a classification of 0 or 1.

My approach uses the following steps:

  1. Partition the training data into k = 5 folds, each containing 200 observations. Let's label each fold data_1, data_2, data_3, data_4, data_5.

    For k = 1, our train_set_k is comprised of data_1, data_2, data_3, and data_4, and will contain 800 observations. Our test_set_k is comprised of data_5, and will contain 200 observations.

    For k = 2, our train_set_k is comprised of data_1, data_2, data_3, and data_5. Our test_set_k is comprised of data_4.

    Etc.

  2. For k = 1 to 5, partition train_set_k into k_i = 5 folds, each containing 800/5 = 160 observations. Cross-validate on these k_i = 5 folds to find the optimal setting for the hyper-parameter lambda for our LASSO logistic regression model. Lambda should be a floating point number between 0 and 1.

    Question #1: I'm unclear as to what "cross-validate to find the optimal setting for the hyper-parameter lambda" actually entails. In R, I'm using the following code:

    model.one.early <- cv.glmnet(x.early, y.early, family = "binomial", nfolds=5, 
                                 type.measure="auc")
    

    ..where nfolds = 5 pertains to the k_i = 5 above. In other words, each of the nfolds = 5 folds will contain 160 observations.

    From this code, I'm able to output values for: "lambda.min" - the value of lambda that gives the minimum mean cross-validated error, which I assume to mean "gives the maximum mean cross-validated AUC," as I specified type.measure = "auc" above; "lambda.1se" - the largest value of lambda such that error is within 1 standard error of the minimum."

    Question #2: What is the above line of code actually doing? How does it compute values of lambda.min and lambda.1se?

    Question #3: Which value of lambda (lambda.min or lambda.1se) do I want to keep? Why?

  3. Fit a LASSO logistic regression model to the 800 observations in this fold using hyper-parameter lambda.min (or lambda.1se) as obtained above. Use this model to predict on the remaining 200 observations, using a piece of code like this:

    early.preds <- data.frame(predict(model.one.early, newx=as.matrix(test.early.df), 
                              type="response", s="lambda.min"))
    
  4. Compute an AUC for these predictions.

  5. Once the above loop finishes, I should have a list of k = 5 lambda.min (or lambda.1se) values, and a list of 5 corresponding AUC values. To my understanding, by taking an average of these k = 5 AUC values, we can obtain an "estimation of the generalisation performance for our method of generating our model." (-Dikran Marsupial, linked here)

  6. This is where I'm confused. What do I do next? Again, I'd like to make predictions on a separate, un-labeled testing set. From what I've read, I must ultimately fit my LASSO logistic regression model with all available training data, using some code like this:

    final_model <- glmnet(x=train_data_ALL, y=data_responses, family="binomial")
    

    Question #4: Is this correct? Do I indeed fit one single model on ALL of my training data?

    Then, I'd simply using this model to predict on my testing set, using some code like this:

    finals_preds <- predict(final_model, newx=test_data_ALL, type="response",  
                            lambda=?)
    

    In my nested cross-validation employed in steps 1 through 5, I've obtained a list of 5 values of lambda and 5 corresponding AUC values.

    Question #5: Which value of lambda do I choose? Do I select the value of lambda that gave the highest AUC? Do I average the k = 5 values of lambda, and then plug this average into the above line of code for lambda = ?.

    Question #6: In the end, I just want one LASSO logistic regression model, with one unique value for each hyper-parameter ... correct?

    Question #7: If the answer to Question #5 is yes, how do we obtain an estimate for the AUC value that this model will produce? Is this estimate equivalent to the average of the k = 5 AUC values obtain in Step 5?

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To answer your initial question (What to do AFTER Nested Cross-Validation?):

Nested cross-validation gives you several scores based on test data that the algorithm has not yet seen. Ordinary CV ("non-nested") gives you just one such score based on one held-out test set. So you can better evaluate the true performance of your model.

After nested CV you fit the chosen model on the whole dataset. And then you use the model to make predictions on new, unlabeled data (that are not part of your 1000 obs.).

I'm not 100% sure that you perform proper nested CV with an outer and an inner loop. To understand nested CV, I found this description helpful: enter image description here

(Petersohn, Temporal Video Segmentation, Vogt Verlag, 2010, p. 34)

Thoughts on bootstrapping as a better alternative than (nested) CV can be found here.

P.S.: I presume that you will more likely get answers if you only ask 1 or 2 questions instead of 7 in one post. Maybe you want to split them up so that others can find them more easily.

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  • $\begingroup$ Thanks a lot for a great answer tobip. The above question was definitely posed during my "brain-scramble to understand basic model validation topics." Fortunately, it's all straightened out at this point. Will do my best to stick to fewer questions next time. $\endgroup$ – cavaunpeu Aug 7 '14 at 15:01
  • $\begingroup$ This is a great answer, but I still have one related question. What if the parameter set p giving the minimum test error (i.e. etilda_cv in step 10 in algorithm) is not the same in each iteration of the outer cross-validation? In this case, the outer CV test error e_cv doesn't seem very meaningful as a performance statistic because it would average the error for different hyper-parameter settings. It seems to me that it would be better to choose the parameter set that most often gives the minimum etilda_cv [i.e. the mode of the set P := {arg min (etilda_cv_i)} where i indexes the iterations... $\endgroup$ – robguinness Nov 19 '14 at 14:52
  • $\begingroup$ ...of outer cross-validation as in the algorithm above]. The outer CV test error could then be calculated as e_cv = (1/m) sum(e_m) from the set of test error values resulting from the same parameter set but different test sets. $\endgroup$ – robguinness Nov 19 '14 at 14:55
  • $\begingroup$ It's unclear what this answer means by "chosen model". If it means something like "model with hyperparameters that gave the best outer performance", I don't think that is correct. $\endgroup$ – user0 May 24 '17 at 23:24
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Question #5: Which value of lambda [of all the λs returned for the different surrogate models] do I choose?

Obviously, if the λs are practically the same, there is no difficulty as there is essentially no choice involved.

If the λs you find for the different surrogate models bounce all over the place, you are in trouble: that is a symptom that either your sample size is too small to auto-tune λ based on your data set, or that the models are (still) very unstable. In both cases, IMHO you'd need to step back and think again about your modeling approach.

I encounter nearly only situations with extremely small sample sizes in my work. Therefore, I'd always decide for the hyperparameter that yields the least complex model (of any kind of regularization).
While your situation may be different, the fact that you use the LASSO indicates that there is a problem with model complexity, so I guess that would be a sensible approach for you as well.

Question #3: Which value of lambda (lambda.min or lambda.1se) do I want to keep?

The same reasoning about model complexity applies. I'd go for lambda.1se.

Question #6 [and #4]: In the end, I just want one LASSO logistic regression model, with one unique value for each hyper-parameter ... correct?

yes

Question #7: If the answer to Question #5 is yes, how do we obtain an estimate for the AUC value that this model will produce? Is this estimate equivalent to the average of the k = 5 AUC values obtain in Step 5?

No it is not the AUC from step 5. This is measured by the outer loop of the nested validation.

I think it is easiest to think of your model training as including the autotuning of λ. I.e. write a training function that does all that is necessary to auto-tune λ using e.g. [iterated] cross validation and then return a model trained on all data that is handed to the training function. Perform the usual resampling validation for these models.

Questions #1 and #2

... are best answered by reading the code: you work in R, so you can read the code and even work though it step by step. In addition, read up what the Elements of Statistical Learning say about hyperparameter tuning. AFAIK, that book is the origin of the 1SE idea for hyperparameter tuning.


Long anwer:

The key idea behind the lambda.1se is that the observed error is subject not only to bias but also to random error (variance). Just picking the lowest observed error risks "skimming" variance: the more models you test, the more likely is observing an accidentally good looking model. lambda.1se tries to guard against this.

There are (at least) 3 different sources of variance here:

  • finite test set variance: the actual composition of the finite test set for the surrogate model in question ("finite test set error")
  • model instability: the variance of the true performance of the surrogate models around the average performance of models of that training sample size for the given problem
  • variance of the given data set with respect to all possible data sets of size $n$ for the given problem.
    This last type of variance is important if you want to compare e.g. algorithms, but less so if you want to estimate the performance you can achieve for the data set at hand.

The finite test set variance can be overwhelmingly large for small sample size problems. You can get an idea of the order of magnitude by modeling the testing procedure as a Bernoulli trial: you can know the size of this variance as it is tied to the observed performance. You could in principle construct confidence intervals around your observed performance using this, and decide to use the least complex model that cannot reliably be distinguished from the best performace observation you got. This is basically the idea behind lambda.1se.

Model instability causes additional variance (which by the way increases with increasing model complexity). But usually, one characteristic of a good model is that it is actually stable. Regarding the λ, stable models (and a data set that is large enough to do the estimation of λ reliably) imply that always the same λ would be returned. The other way round, λs that vary a lot indicate that the optimization of λ was not successful.

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