Description: High bias refers to a model that makes strong assumptions about the data, leading to underfitting. This phenomenon occurs when a model is too simple to capture the complexity of the underlying data, resulting in poor performance on both the training and testing sets. High-bias models tend to overlook relevant patterns, which can lead to inadequate generalization. For example, a linear model trying to fit data that follows a quadratic relationship may fail to capture the true nature of the relationship, resulting in inaccurate predictions. Key characteristics of high bias include an inability to learn from the data, a high error rate on training, and a lack of flexibility to adapt to new information. This concept is fundamental in the field of machine learning, as it highlights the importance of finding a balance between model complexity and the available data. High-bias models may result from inappropriate algorithm choices, inadequate feature selection, or a lack of sufficient data to properly train the model. In summary, high bias is a critical challenge in model evaluation, as it can limit a system’s ability to make accurate and useful predictions.
