Likelihood Function

In statistics, the likelihood function is a function of the parameters of a statistical model given a set of observed data. It measures the goodness of fit of the model to the data and is used to estimate the values of the parameters that maximize the probability of observing the data. The likelihood function is a fundamental concept in statistical inference and is used in maximum likelihood estimation, which is a method for finding the best estimates of the parameters of a model. The likelihood function is often used in machine learning algorithms, such as logistic regression and neural networks, to train models and make predictions.
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