If a machine learning model shows consistent predictions despite variations in input features, what could be the underlying reason for this behavior?

When a model keeps giving the same answer even though the inputs change, it usually means the model is not using those inputs at all. This can happen if the model is too simple, like a linear regression with all but one coefficient set to zero, so only one feature matters. It can also be the result of heavy regularization that forces most weights to shrink toward zero, making the model insensitive to variations. In practice, you might see a model that always predicts the mean of the training data because it has learned that no input feature helps improve accuracy. For example, a decision tree that stops splitting after the first node will output the same prediction for every input, regardless of the remaining features.