Data Science Asked by dwkd on August 8, 2021
I have 10 categorical features and a multi-class target.
Training data contains rows where the same 10 categorical features may map to a different target class.
What classification algorithm should I choose that fulfils the following criteria:
I realize this looks like it could be ‘easily’ solved with databases but I would like to use Classification and train a model instead of having a cumbersome db infrastructure with columns, keys, indexes and other boring things.
Strictly speaking Machine Learning is not the answer to this problem imho, because a ML method always works by generalizing what is in the data. In other words ML methods are meant to make some guesses while statistically minimizing the risk of error: if you don't want any generalization or risk of error, you don't want ML.
There might be some symbolic methods which could do what you describe, but essentially it's just a very simple deterministic method:
a cumbersome db infrastructure with columns, keys, indexes and other boring things.
There's a confusion here: the question of using a database or not is irrelevant here since it's a matter of how you store the data, it's just not related to using ML or not.
Usual advice: use the right tool for the right job... even if it's boring ;)
Answered by Erwan on August 8, 2021
Get help from others!
Recent Questions
Recent Answers
© 2024 TransWikia.com. All rights reserved. Sites we Love: PCI Database, UKBizDB, Menu Kuliner, Sharing RPP