The correct answer is: B. Systematic errors in model predictions.
Bias is a systematic error in a model’s predictions. It is caused by a model’s tendency to over- or under-predict the value of a target variable. Bias can be caused by a number of factors, including the way the model is trained, the data that is used to train the model, and the assumptions that are made about the data.
Variability in model predictions is the degree to which the model’s predictions change when the data is changed. Variability can be caused by a number of factors, including the amount of data that is used to train the model, the noise in the data, and the complexity of the model.
Data preprocessing is the process of cleaning and preparing data for use in machine learning. Data preprocessing can include tasks such as removing duplicate data, filling in missing values, and normalizing the data.
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