Linked Questions

3
votes
5answers
618 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
2answers
1k views

Model prediction: test for difference in MSE

I have made two regression models. They were made on a training set of 80% of the data. And 20% of the data is the validation set. No test set is made. The models tell me how much premium a ...
3
votes
1answer
1k views

SVM parameter selection and model testing with cross-validation

I've read: Model selection and cross-validation: The right way Crossvalidation and/or testdata. Always use both or can one exclude the other? but I still don't get it. My problem is to construct a ...
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 ...
1
vote
1answer
985 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 ...
0
votes
1answer
812 views

Model selection: before or after nested cross-validation?

I want to build a neural network over a data set. My idea is to use cross-validation on a training set to select the "best" neural network (and evaluate it on a separate test set) and to use nested ...
1
vote
2answers
876 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 ...
3
votes
1answer
865 views

Compare different classification algorithms after hyperparameter tuning

Let's say I have a classification problem with $c$ classes. For this, I have a data set containing $N$ distinct feature vectors with $n$ features. Let's say $N$ is of the order of $10^5$, and both $c$ ...
4
votes
1answer
407 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 ...
6
votes
1answer
859 views

Nested cross validation vs repeated k-fold

I know there are many topics(1,2,3), papers(1,2,3) and websites(1) that discuss this topic at length. However for the past two days I am reading all I can find about the subject and seems I hit a ...
5
votes
2answers
489 views

Which gamma regression model to use for extrapolation?

I'm looking for a regression model which would satify these requirements: My target variable follows the exponential distribution, so to my understanding I should use gamma loss function. I have ...
2
votes
1answer
470 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
739 views

parameter tuning using nested cross validation

Parameter tuning in SVM has been performed using a nested cross-validation(CV) approach with 45 folds(outer loop) and 13 folds(inner loop). In this process, the outer loop will have 45 prediction ...
5
votes
1answer
425 views

What is the standard procedure for evaluating a user-based CF algorithm with a dataset offline?

I have read some papers and other materials about the evaluation of recommender systems (RS). Most of them discuss the various properties of RS (e.g. accuracy, diversity, etc.), and metrics for ...
1
vote
1answer
631 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 ...

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