So, in summary, the key components of achieving good generalization (not underfitting or overfitting) in machine learning are hyper-parameter search, regularization, and out-of-sample testing.

Clear, visual explanation of the bias-variance tradeoff and how to find the sweet spot in your models. #BiasVariance #Overfitting #MachineLearningBasics ...

Overfitting is a more frequent problem than underfitting and typically occurs as a result of trying to avoid overfitting.