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Maximum Likelihood Estimation (MLE) is a method for fitting logistic regression models. It works by finding the values of the model’s parameters (coefficients) that make the observed data most probable.
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In logistic regression, MLE determines the probability of an event occurring based on the relationship between predictor variables and a binary outcome.
This involves maximising a likelihood function, which measures how well the model fits the data. Once the best-fitting parameters are identified, the model can predict probabilities of outcomes with greater accuracy.
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