Machine Learning, Model Selection, Hyperparameter Tuning, Learning Curve, Cross Validation.
illustrate the concept of (statistical) bias and variance. Bias in this context refers to erroneous (e.g. simplifying) model assumptions, which can cause the model to underfit the data. A high-bias model does not adequately capture the structure present in the data. Variance on the other hand