Underfitting
Underfitting occurs when a Machine learning model is too simple to capture the underlying patterns in the Data it is trained on, resulting in poor performance on both the training and test sets. This can happen when the model has too few parameters or is not complex enough to learn the relationships between the input variables. Underfitting is often characterized by a high bias and a low variance. It is a common problem in Supervised learning and Unsupervised learning tasks, and can be addressed by increasing the complexity of the model, adding more features, or using regularization techniques. Andrew Ng and other experts in the field of Artificial intelligence have discussed the importance of finding the right balance between model complexity and simplicity to avoid underfitting and Overfitting.