Model diagnostics are crucial for several reasons:
Validity: They help determine if the model accurately represents the underlying data. Reliability: They ensure that the model's predictions are consistent and reproducible. Generalizability: They assess whether the model can be applied to other datasets or populations. Error Identification: They help identify and correct errors or biases in the model.
