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The Model Complexity Myth
FromData Skeptic
ratings:
Length:
30 minutes
Released:
Sep 11, 2015
Format:
Podcast episode
Description
There's an old adage which says you cannot fit a model which has more parameters than you have data. While this is often the case, it's not a universal truth. Today's guest Jake VanderPlas explains this topic in detail and provides some excellent examples of when it holds and doesn't. Some excellent visuals articulating the points can be found on Jake's blog Pythonic Perambulations, specifically on his post The Model Complexity Myth.
We also touch on Jake's work as an astronomer, his noteworthy open source contributions, and forthcoming book (currently available in an Early Edition) Python Data Science Handbook.
We also touch on Jake's work as an astronomer, his noteworthy open source contributions, and forthcoming book (currently available in an Early Edition) Python Data Science Handbook.
Released:
Sep 11, 2015
Format:
Podcast episode
Titles in the series (100)
[MINI] Cross Validation: This miniepisode discusses the technique called Cross Validation - a process by which one randomly divides up a dataset into numerous small partitions. Next, (typically) one is held out, and the rest are used to train some model. The hold out set can... by Data Skeptic