Parameter Estimation
Parameter estimation is the process of using data to estimate the parameters of a statistical model describing the relationship between variables. In data science and artificial intelligence, parameter estimation is a crucial step in building and training models. It involves finding the best values for the parameters of a model that minimize the difference between the predicted values and the actual values of the target variable. This is typically done using optimization algorithms such as gradient descent. The accuracy of the parameter estimates can have a significant impact on the performance of the model, and therefore, it is important to choose appropriate estimation methods and evaluate the quality of the estimates. In machine learning, parameter estimation is often performed using techniques such as maximum likelihood estimation, Bayesian inference, and regularization.
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