Linked Questions

Firstly, apologies for posting a question that has already been discussed at length here, here, here, here, here, and for reheating an old topic. I know @DikranMarsupial has written about this topic ...

This might sound like a very simple question, but I haven't been able to find an answer to it, yet: Assuming I am working on a binary classification task and I am using k-fold cross-validation to ...

this is a popular topic here but I have been reading through the different pages and could not find anything related with what I am wondering now. So, I have a data set with X features and I would ...

I have read the following posts for nested cross validation and still am not 100% sure what I am to do with model selection with nested cross validation: Nested cross validation for model selection ...

Can anyone tell me what exactly a cross-validation analysis gives as result? Is it just the average accuracy or does it give any model with parameters tuned? Because, I heard somewhere that cross-...

I am using LASSO and PCA for performing feature selection on a classification problem. The dataset consist of 20 features and around 5.7k observations. One of the reviewer comments for this approach ...

In general, if we have a large dataset, we can split it into (1) training, (2) validation, and (3) test. We use validation to identify the best hyperparameters in cross validation (e.g., C in SVM) and ...

Background: Train set: data used to train the chosen model Dev set: data used to tune the model's parameters Test set: data used to evaluate the performance of the final model How cross-validation ...

There are several questions on this site essentially asking how nested cross validation for parameter tuning works. A lot of the answers use some jargon that I find difficult to understand, but as far ...

I have my data stratified in 10 folders. So far I was using 9 of them to train the model, and the remaining one for testing it. A sudden thought just crossed my mind saying "you might be cheating". ...

I am having issues understanding some concepts of algorithm comparison/parameter optimization/cross-validation in R Let's say I want to compare two classification algorithms, such as Random Forests ...

I have about 150 samples 1000 features (ranked by their importance by Relieff score). My question is, what would be the best approach to: choose the hyper parameters choose the optimal number of ...

Suppose I am fitting a Ridge and I decide to search a parameter space for c:[1,2,3]. I perform nested CV on my whole dataset and find the performance not so great. I therefore expand my search space ...

I am reading Cawley and Talbot and saw a post on implementation of nested CV (NCV) as well as numerous posts with good answers on the topic (general NCV, training with full dataset after NCV, how to ...

For my class project I am comparing various tree-based ensemble methods such as bagging, boosting, random forest, and AdaBoost against my data set and I can't quite determine my methodology. I know ...