What's the difference between being overfit and being underfit?

Data Science, AI and ML
#1

Overfitting – In overfitting, a statistical model describes any random error or noise, and occurs when a model is super complex. An overfit model has poor predictive performance as it overreacts to minor fluctuations in training data.
Underfitting – In underfitting, a statistical model is unable to capture the underlying data trend. This type of model also shows poor predictive performance.