Expectation Step

The Expectation Step (E-step) is part of an algorithm used to find hidden patterns in data, such as in clustering or classification. It involves estimating the probability that each data point belongs to different groups based on current model parameters. Think of it as making educated guesses about how data points might be grouped, considering the existing understanding. These probabilities then inform the next step, which updates the model to better fit the data. Essentially, the E-step computes expected group assignments, helping the algorithm improve iteratively.