Linked Questions

17
votes
1answer
2k views

How to build the final model and tune probability threshold after nested cross-validation?

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 ...
3
votes
5answers
628 views

Cross-validation: Which classifier to use in the end? [duplicate]

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 ...
1
vote
1answer
657 views

Feature selection & Cross Validation

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 ...
16
votes
3answers
5k views

How to get hyper parameters in nested cross validation?

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 ...
17
votes
4answers
18k views

Cross validation and parameter tuning

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-...
1
vote
1answer
345 views

Feature selection using LASSO and PCA on training data or whole data?

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 ...
8
votes
1answer
2k views

How to obtain optimal hyperparameters after nested cross validation?

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 ...
1
vote
2answers
906 views

Cross-validation when splitting data into train/dev/test sets

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 ...
5
votes
1answer
437 views

An intuitive understanding of each fold of a nested cross validation for parameter/model tuning

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 ...
2
votes
5answers
1k views

10-fold cross validation, why having a validation set?

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". ...
1
vote
1answer
992 views

Comparing classification algorithms using cross validation and caret's train

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 ...
1
vote
1answer
1k views

Feature selection: is nested cross-validation needed?

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 ...
2
votes
1answer
475 views

Does changing the parameter search space after nested CV introduce optimistic bias?

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 ...
1
vote
1answer
174 views

Toy implementation of nested cross-validation: how to determine number of inner and outer folds and how many iterations to run?

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 ...
0
votes
1answer
178 views

How to validate a model when first exploring model hyperparameter space?

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 ...

15 30 50 per page