Prior Probability
Prior probability is the probability of an event occurring before taking into account new evidence. In data science, prior probability is used in Bayesian statistics to calculate the probability of a hypothesis being true before any data is collected. It is based on the available knowledge or belief about the event before any evidence is considered. Prior probability is updated as new evidence is collected, and the updated probability is called posterior probability. Prior probability is an important concept in machine learning, where it is used to estimate the probability of a new data point belonging to a particular class based on the prior probabilities of the classes and the features of the data point.
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