Removing redundant or irrelevant inputs from a training data set often reduces what issue?

Reducing redundant or irrelevant inputs from a training data set primarily addresses the issue of overfitting. When a model is overfitted, it learns to capture noise and random fluctuations in the training data rather than general patterns that would apply to unseen data. This often happens when the model is overly complex, with too many features, some of which may provide no valuable information or may reflect random noise.

By eliminating unnecessary inputs, the model focuses on the most informative features, which can help it generalize better to new data. This streamlining leads to a simpler, more interpretable model that can perform better on unseen datasets. Thus, by reducing the quantity of inputs, the risk of fitting the model too closely to the training data is mitigated, allowing the model to maintain performance on new instances.

In contrast, underfitting occurs when the model is too simple to capture the underlying trends in the data. Bias refers to the error due to overly simplistic assumptions in the learning algorithm itself, while variance refers to the model's sensitivity to fluctuations in the training data. While these concepts are related, the act of removing irrelevant features specifically targets the complexity of the model and influences its tendency to overfit the training data, making it the most relevant option in