Evaluating prediction results is vital for assessing model performance. Key metrics for classification include accuracy, precision, recall, and F1 score, while regression tasks utilize Mean Absolute Error, Mean Squared Error, and R-squared. Visualization techniques like confusion matrices and scatter plots enhance understanding and reveal patterns in predictions.