Compare the advantages and disadvantages of eager classification methods $($e$.$g$.,$ decision tree, Bayesian, neural network$)$ versus lazy classification methods $($e$.$g$.,$ k$-$nearest neighbor, case$-$based reasoning$).$ In your discussion, consider factors such as training time, prediction time, memory usage, and accuracy. Provide examples to illustrate how these different approaches perform in real$-$world scenarios, and discuss situations where one might be preferred over the other.