Using K-Fold Cross Validation with Keras (5.2)

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K-Fold cross validation is an important technique for deep learning. This video introduces regular k-fold cross validation for regression, as well as stratified k-fold for classification. Cross-validation can be used for a wide array of tasks, such as error estimation, early stopping, and hyper-parameter optimization.

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  1. Jeff Heaton
  2. python
  3. python introduction
  4. python introduction deep learning
  5. deep learning
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Комментарии
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This is a great video. I am just finishing up my degree in computer engineering and was stuck on an assignment until I watched this video. It has great production quality and you provide enough information to the user for them to understand what's actually going on. Thanks for this!

thatguycalledaustin
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Thank you! This was explained so well and clear. I was looking for a solution all over the internet and finally found one! lets get coding...

BlackBlingGirl
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Thanks so much for the video, I didn't know about this, I love the way scikit learn integrates with other frameworks, keep it up!

I wish I could took this course in person :)!

MrCutter
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Hi, thank you for your amazing video series!
One thing I'm wondering about is the fact that you are using your validation data as the test data. I read that the validation data doesn't influence the model in its learning phase. I just want to ask whether it is fine and formally correct to use your validation data as the test data?

felixmuller
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In the first part of your study, you standardized the income, aspect, save_rate and subscriptions columns. Why did you standardize only these columns?

kaanakdik
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Crack! Like a good teacher you teached me the intuition and answered the question.. Good i run my k-fold algorithm but now what?, what kind of decissions could i take with this?. Really thanks ! Because on internet people talk mainly about the algorithm but no about the decisions

klausrichter
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Hi, in the last example, which model (out of the five models) is used for the holdout data?

seyedmostafahallaji
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How to compare or draw the loss and val_loss in this case?

alisaghi
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Hey Jeff, it would be awesome if you could show how to go about combining the models into a single one rather than voting

zebcode
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how do you do the normalization with the holdout- is the X_main used to find the scaling and offset and then that is applied to the holdout?

joshuamills
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Hey Jeff! I hope I get a reply as early as possible because I'm working on a deep learning project. So, my dataset is actually image frames (residing in a separate folder) with corresponding XML annotation files in another folder. I want to train a model for object detection. Can I use K fold cross-validation in such kind of dataset? If yes, how? If not, then what would be the best approach?

GeekGenius
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Will the kfoldstratify reduce the problem of imbalanced dataset in case of multi class classification ? Or is it required to perform SMOTE apart from kfoldstratify to over the program of imbalanced dataset ?

pradeepgb