What are the limitations of statistics in machine learning?

What are the main limitations of using statistics in machine learning?

Some limitations include overfitting, reliance on assumptions, and difficulty in capturing complex patterns.

Can you explain how overfitting affects model performance?

Overfitting occurs when a model learns noise in the training data instead of the underlying pattern, leading to poor generalization.

What assumptions do statistical methods make that might not hold in real-world data?

Statistical methods often assume linearity, normality, and independence of observations, which may not be true in practice.

How can we mitigate these limitations in machine learning?

We can use techniques like cross-validation, regularization, and ensemble methods to reduce overfitting and improve model robustness.