Machine Learning, Python, Data Science, Artificial Intelligence, Deep Learning.
actual values. MSE penalizes larger errors more severely due to the squared term. Pros: 1. Differentiable, making it suitable for optimization algorithms. 2. It gives higher weightage to larger errors, which can be useful when you want to focus on minimizing significant errors. Cons: 1. Not robust