Bias-Variance Trade-Off 편집하기 (부분)

==Understanding Bias and Variance==
*'''Bias''': Refers to the error introduced by approximating a complex real-world problem with a simplified model. High bias typically results from underfitting, where the model is too simple to capture the underlying patterns in the data.
**Example: A linear model trying to fit highly nonlinear data is likely to have high bias.

*'''Variance''': Refers to the error caused by the model’s sensitivity to small fluctuations in the training data. High variance generally results from overfitting, where the model captures noise and specific patterns in the training data that do not generalize well.
**Example: A decision tree with many splits may have high variance, as it is likely to fit noise in the training data.