Logic often states that by underfitting a model, it's capacity to generalize is increased. That said, clearly at some point underfitting a model cause models to become worse regardless of the complexity of data.How do you know when your model has struck the right balance and is not underfitting the data it seeks to model?Note: This is a followup to my question, Why Is Overfitting Bad?
When is a Model Underfitted?
efficiency;algorithms;parameter