Is it correct to retrain the model on the whole training set?

Indeed, it should be avoided to train several models on the training set and test each of them on the test set in order to pick the one with the best accuracy. This is a common mistake as it leads to overfitting on the test set and therefore to inflated results on the test set.

How would you arrange the dataset for your learning algorithm training cross validation and testing?

The best approach is to select/arrange data randomly. Basically you have three data sets: training, validation and testing. You train the classifier using ‘training set’, tune the parameters using ‘validation set’ and then test the performance of your classifier on unseen ‘test set’.

Why is it important to have separate train and validation sets?

In the training process, if all training dataset is used once, there may be a problem with over-fitting. In order to avoid this over-fitting issue, the training dataset is often separated for cross validation (CV) process. Validation set is optional, and it is aimed to avoid over-fitting problem.

Do you’re train on the whole dataset After validating the model?

So the answers to your question are (i) yes, you should use the full dataset to produce your final model as the more data you use the more likely it is to generalise well but (ii) make sure you obtain an unbiased performance estimate via nested cross-validation and potentially consider penalising the cross-validation …

Why do we need to retrain the model?

We can experience data drift, concept drift, or both. To stay up to date, the models should re-learn the patterns. They need to look at the most recent data that better reflects reality. That is what we call “retraining”: adding new data to the old training pipelines and running them once again.

Why is cross validation better than a simple split of the data into training and holdout test partitions?

Cross-validation. Cross-validation is usually the preferred method because it gives your model the opportunity to train on multiple train-test splits. This gives you a better indication of how well your model will perform on unseen data.

Do we need train test split for cross validation?

EDIT: For doing k-fold cross-validation, you don’t need to split the data into training and validation set, it is done by splitting the training data into k-folds, each one of which will be used as a validation set in training the other (k-1) folds together as training set.

Do you need to split data for cross validation?

You need to split your data into training and testing subsets for cross-validation. In k-fold cross-validation you do it k times repeatedly.

Is it necessary to have validation set?

As you have already decided on the model beforehand, validation set is not needed.