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Results tagged with training
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user 45264
Training is the part of machine learning whereby a model is "trained" on a define portion of a dataset to learn attributes and statistical features of the data. It's counterparts are called Testing and Validation. After training a model is tested and validated on another portion of the dataset.
1
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How do I control for some patients providing multiple samples in my training data?
Stratification simply means to create training/test splits, which do their best to preserve class balance. … So if you select one feature as most important, stratification will make sure that training and test sets each have a proportional number of samples exhibiting that feature. …
- 15.1k
1
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Which is more important - stable training results or good test results?
If would imply that your training and test data do not originate from the same underlying distribution! … If the test accuracies are all over the place, also varying dramatically, then I would definitely want to sort out the training curves first. …
- 15.1k
0
votes
Normalization before or after resizing
I don't think there will be a huge difference... although it will depend on how small you resize. The resizing is doing some kind of reduction and/or necessary interpolation (depending on the implemen …
- 15.1k
9
votes
Accepted
How to train data by batch from disk?
As you are working on image classification and would also like to implement some data augmentation, you can combine the two AND load the batches directly from a folder using the mighty 'ImageDataGener …
- 15.1k
1
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Algorithm to calculate nerual network training time?
The best way to know is to train two epochs. The first epoch often takes longest, because data loading takes place with some caching.
The second epoch will give you an accurate time for each epoch.
…
- 15.1k
23
votes
Is it always better to use the whole dataset to train the final model?
So the question as to whether re-training on the full dataset will improve performance on future unseen data is not strictly something you can test. … the final re-trained model again on the original test set; expecting that it scores higher than it ever did when the model only saw the train/val set, because it has actually seen the test set during training …
- 15.1k
1
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400 positive and 13000 negative: how to split dataset up (train, test, validation)
One could imagine splitting to have e.g. 300/9750 (pos/neg) in the training dataset, and during training, you create stratified batches from those 1050 images, so each batch e.g. of 50 images, might contain …
- 15.1k
3
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What happens to the left over unpicked data in Random Forest
I suppose it is possible that not all samples are selected during training, depending on the parameters you specify (or that are available in the implementation). …
- 15.1k
2
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Does save_best_only in Keras prevents overfitting?
It really only tracks the value of the metric you selected, there is no tolerance option. In the relevant documentation, the definition is given:
save_best_only: if save_best_only=True, the latest …
- 15.1k
6
votes
Very Fast Training After First Epoch
The timing differences mentioned were between the first epoch of training and the remaining epochs. … The model and so the computational graph is compiled only once, when you call model.compile(), which is not part of the training itself. …
- 15.1k