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Should validation data be shuffled?
So, it should not make any difference whether you shuffle or not the test or validation data (unless you are computing some metric that depends on the order of the samples), given that you will not be computing any gradient, but just the loss or some metric/measure like the accuracy, which is not sensitive to the order …
What is a good train-test split?
Split your data into training and testing (80/20 is indeed a good starting point) Split the training data into training and validation (again, 80/20 is a fair split). Subsample random selections of your training data, train the classifier with this, and record the performance on the validation set.
Why should the data be shuffled for machine learning?
In machine learning tasks it is common to shuffle data and normalize it. The purpose of normalization is clear (for having same range of feature values). But, after struggling a lot, I did not find any valuable reason for shuffling data.
Why do we need to shuffle data in a neural network?
If not shuffling data, the data can be sorted or similar data points will lie next to each other, which leads to slow convergence: For best accuracy of the model, it’s always recommended that training data should have all flavours of data. Shuffling of training data helps us in achieving this target.
How can random state affect machine learning results?
With some manipulation to the random permutation of the training data and the model seed, anyone can artificially improve their results. In this article, I would like to gently highlight an often overlooked component of most data science projects — random state and how it affects our model outputs in machine learning.
Why do we need to shuffle data in minibatch?
We need to shuffle only for minibatch/SGD, no need for batch gradient descent. If not shuffling data, the data can be sorted or similar data points will lie next to each other, which leads to slow convergence: For best accuracy of the model, it’s always recommended that training data should have all flavours of data.