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/Evaluation of Learning Algorithms Using ROC Curve
Evaluation of Learning Algorithms Using ROC Curve
Understand the parameters of Maximum Likelihood Estimation (MLE) and Maximum A-Posteriori Estimation (MAP) algorithms in Python and learn how to use ROC curve to evaluate their performance.
We'll cover the following...
In this lesson, we will introduce the evaluation of the ROC curve for several parameter combinations, providing a quantitative measure of learning output performance. This analysis will help us understand not just the quality of the CPDs generated but also the predictive power and reliability of each algorithm.
First, we dive into the main parameters that we can use.
Bayes prior
The bayes_prior parameter in the Bayesian estimation methods for learning the ...