Description: High variance refers to a machine learning model that is overly complex and thus able to capture not only the underlying trends in the training data but also the noise and random fluctuations. This results in a phenomenon known as overfitting, where the model fits too closely to the training data and loses its ability to generalize to new data. Models with high variance tend to show exceptional performance on the training set, but their performance degrades significantly when evaluated on a test dataset. The main characteristics of high variance include model complexity, sensitivity to small variations in data, and inability to generalize. This phenomenon is especially common in high-capacity models, such as deep decision trees or neural networks with many layers. High variance is a critical challenge in model evaluation, as it can lead to erroneous decisions in the implementation of models in various applications. To mitigate high variance, techniques such as regularization, reducing model complexity, or using cross-validation methods can be employed to ensure that the model remains robust and generalizable.
