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Micro Signal: Machine Learning Bee
This is the final lesson in the Cornerstones of Machine Learning. The main focus is on three very important and extremely easy to overlook principles in machine learning.
Three principles of machine learning
entia non sunt multiplicanda praeter necessitatem
(entities must not be multipliedbeyond necessity)—William of Occam (1287-1347)
Corresponding to machine learning, theSimple model first (linear first)
If the data is sampled in a biased way, learning will produce a similarity biased outcome
Sampling bias, especially at validation, should be avoided.
If a data set has affected any step in the learning process, its ability to assess the outcome has been com- promised.
It is especially important to avoid the human eye prying into the data.
careful balance between data-driven modeling (snooping) and validation (no-snooping)
This post introduces three important principles in machine learning.
This concludes the Machine Learning Cornerstones class and will be followed by a hands-on machine learning system, so stay tuned!
Finally, thank you from the bottom of my heart, Mr. Lin, for watching this course and being enlightened.
1. Screenshot of this article from Mr. Lin Tianxuan's class
Thank you for your interest in Machine Learning Bee, and I look forward to your comments and suggestions.
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