PyTorch K-Fold Cross Validation
K-Fold Cross Validation is a technique used to assess the performance of a machine learning model. This technique involves dividing the dataset into k subsets, or folds, and then using k-1 subsets for training the model and the remaining subset for testing the model. This process is repeated k times, each time with a different subset as the test set. The results are then averaged to get a more reliable estimate of the model’s performance.
PyTorch is a popular deep learning framework that provides tools for building and training neural networks. One of the ways to implement K-Fold Cross Validation in PyTorch is by using the KFold class from the scikit-learn library.
Here is an example of how to perform K-Fold Cross Validation in PyTorch:
import torch
from sklearn.model_selection import KFold
# Assuming we have a dataset called 'data'
data = torch.randn(100, 10)
# Define the number of folds
k_folds = 5
kf = KFold(n_splits=k_folds)
for train_index, test_index in kf.split(data):
train_data, test_data = data[train_index], data[test_index]
# Train and test your model using the train_data and test_data
# Calculate the model performance metrics
In this example, we first create a random dataset called ‘data’. We then define the number of folds to be 5. Next, we create an instance of the KFold class with the desired number of folds. We iterate over the train and test indices generated by the KFold object and use them to split the dataset into training and testing sets. We can then train and test our model using these sets and evaluate its performance metrics.
Using K-Fold Cross Validation in PyTorch can help us get a better estimate of how well our model generalizes to new data and can prevent overfitting. It is a useful technique for evaluating the performance of machine learning models and can improve the reliability of our results.
Overall, PyTorch K-Fold Cross Validation is a powerful tool that can help us assess the performance of our neural networks more accurately. By implementing this technique in our projects, we can improve the robustness and generalization capabilities of our models.
I noticed for the regression you put 'model = torch.compile(model,backend="aot_eager").to(device)' inside the for loop while for the classification you put it outside the for loop. What's the difference? Thanks.
Hey Jeff, nice video! How do you then typically proceed with the results of the k-fold cross validations? Obviously, for evaluation purposes it is nice to have multiple runs and then make average and standard deviation to have more relieable results. However, at some point you want to deploy a model but now you actually have k models. Some suggest to train once more a model on all data of the cross validation, but I struggle a bit with that praxis since some models like neural network are not necessarily training stable under all circumstances and a training on all data doesnt need to be as successful as the training on the folds. Besides that, you could simple choose one of the k models or make an ensemble out of all k models however this might blow up the consumption of computational resources.
For my cases, I typically do a cross validation for hyper-parameter optimizations, then train a model on all data of the cross valdiation and then select the best model (typically you also compare different algorithms) on a test slice. However, this requires quite a lot data to produce relieable results so I am not that happy with my procedure. I would be highly interested how you handle this situation.
Besides that, I would like to suggest a video topic for you: Setting thresholds of models. The practical implemenation of this is trivial however the determination of the "right" threshold is acutally quite challenging in my opinion especially in dynamic environments and also considering models that overfit/are overconfident on training data like random forrest or neural networks.
Hi Jeff, I've just stumbled across your course and it seems amazing! Am I to understand that the entire material which is taught at your university (including the assignments) is available for free on your github 🙂 ?