The order of samples in a training set can have an impact on model training, but the extent of its influence depends on the type of model and the nature of the data. There are several aspects to consider:

  1. Sequential models: In some cases, the order of samples is critical, especially in sequential models like recurrent neural networks (RNNs) or time series models. These models learn patterns based on the sequential nature of the data. Therefore, the order of samples can affect the learned representations and the model's performance. For example, in language modeling, the position of words in a sentence can significantly impact the meaning and grammatical structure.

  2. Generalization: The order of samples can influence a model's ability to generalize to unseen data. If the training set is sorted in a specific order that does not represent the distribution of the overall data, the model may be biased towards learning patterns specific to that order. This can lead to poor performance on new, diverse samples. Randomizing the order of samples during training can help the model generalize better.

  3. Overfitting: Overfitting occurs when a model becomes too specialized to the training data and fails to generalize well. The order of samples affects the model's exposure to different patterns, potentially impacting the risk of overfitting. In some cases, a specific order may lead to overfitting if the model learns to memorize the patterns in the training set rather than extracting generalizable features. Randomizing the order of samples during training can help mitigate overfitting.

  4. Optimization: The order of samples can interact with the optimization algorithm used during model training. Some optimization algorithms, like stochastic gradient descent (SGD), update the model parameters based on a subset (batch) of samples at each iteration. The order in which these batches are presented to the model can affect the convergence speed and the solution found by the optimization algorithm.

  In conclusion, while the order of samples in a training set can have an impact on model training, its importance varies depending on the model type and data characteristics. Randomizing the order of samples during training is a common practice to avoid biases and promote better generalization.