Prior Knowledge
Prior knowledge refers to any relevant information or experience that an individual possesses before engaging in a particular task or learning activity. In the context of data science and artificial intelligence, prior knowledge can be used to improve the accuracy and efficiency of machine learning algorithms. This can include information about the structure of the data, the relationships between variables, or the underlying distribution of the data. Prior knowledge can be incorporated into machine learning models through techniques such as Bayesian inference, regularization, or feature engineering. By leveraging prior knowledge, machine learning algorithms can make more informed predictions and reduce the risk of overfitting to the training data.
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