Final answer:
Randomness can affect training/validation performance in machine learning algorithms by influencing outcomes during model training and evaluation
Step-by-step explanation:
Randomness can have effects on training/validation performance in machine learning algorithms. When training a model, random initialization of weights and biases can lead to different outcomes, causing variation in the performance metrics. Randomness is also commonly used in techniques like data shuffling, data augmentation, and dropout, which can affect the performance as well.
For example, in neural networks, if the weights are initialized with the same values, the model might converge to a suboptimal solution or get stuck in a local minimum. By introducing randomness in the initialization, the model can explore different solutions and potentially find a better one.
Similarly, when evaluating the performance of a model on a validation set, randomness can influence the results. The random splitting of data into training and validation sets can lead to different subsets of data being used for training and evaluation, impacting the performance metrics.