Expected Calibration Error
Machine Learning
An iterative method for finding maximum likelihood estimates of parameters in statistical models with unobserved variables.
Detailed Explanation
The Expectation-Maximization (EM) Algorithm is an iterative technique used in machine learning to estimate parameters of statistical models containing hidden or unobserved variables. It alternates between the Expectation step, which calculates the expected value of the log-likelihood given current parameters, and the Maximization step, which updates parameters to maximize this expectation, converging to optimal estimates.
Use Cases
- •Refining mixture model parameters for better clustering accuracy in customer segmentation analysis.
Related Terms
Other terms in the Machine Learning category