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LM101-076: How to Choose the Best Model using AIC and GAIC
LM101-076: How to Choose the Best Model using AIC and GAIC
ratings:
Length:
28 minutes
Released:
Jan 23, 2019
Format:
Podcast episode
Description
In this episode, we explain the proper semantic interpretation of the Akaike Information Criterion (AIC) and the Generalized Akaike Information Criterion (GAIC) for the purpose of picking the best model for a given set of training data. The precise semantic interpretation of these model selection criteria is provided, explicit assumptions are provided for the AIC and GAIC to be valid, and explicit formulas are provided for the AIC and GAIC so they can be used in practice. Briefly, AIC and GAIC provide a way of estimating the average prediction error of your learning machine on test data without using test data or cross-validation methods. The GAIC is also called the Takeuchi Information Criterion (TIC).
Released:
Jan 23, 2019
Format:
Podcast episode
Titles in the series (85)
LM101-005: How to Decide if a Machine is Artificially Intelligent (The Turing Test): Episode Summary: This episode we discuss the Turing Test for Artificial Intelligence which is designed to determine if the behavior of a computer is indistinguishable from the behavior of a thinking human being. The chatbot A.L.I.C.E. by Learning Machines 101