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In machine learning, different from  conditional probability,  unconditional probability refers to the probability of an event occurring without any conditions or prior knowledge. It is often denoted as P(A), where A is the event in question. Unconditional probability provides a straightforward measure of the likelihood of an event happening, regardless of other factors. Unconditional probability is a fundamental concept in probability theory and is used in various machine learning algorithms, particularly in tasks related to probability estimation, classification, and decision-making. Mathematically, the unconditional probability of an event A is calculated as the ratio of the number of favorable outcomes for A to the total number of possible outcomes in the sample space. The formula is:

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