Bias
Bias refers to the systematic error in a model's predictions due to the model's inability to capture the true relationship between the input features and the target variable. Bias can occur due to the model's oversimplification of the problem or due to the lack of relevant features in the training data. High bias models are typically underfitting and have poor performance on both the training and test data. In order to reduce bias, one can use more complex models, add more relevant features, or increase the amount of training data.
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