Machine Learning

639 readers

2 users here now

A community for posting things related to machine learning

Icon base by Lorc under CC BY 3.0 with modifications to add a gradient

founded 3 years ago

MODERATORS

Ategon@programming.dev

Akisamb@programming.dev

ericjmorey@programming.dev

How do you choose which ML algo is best for the given problem? (nord.pub)

submitted 1 week ago by bruh@nord.pub to c/machine_learning@programming.dev

2 comments fedilink hide all child comments

Hello,

I am new to ML and stuff. watched a video on yt and found that the instructor was brute forcing every algorithm to classify high energy particles. I was wondering if professional data scientist also do this or there are some rules about which ML model to choose.

( the instructor also used a NN to classify which gave pretty good accuracy )

you are viewing a single comment's thread
view the rest of the comments

[–] howrar@lemmy.ca 2 points 1 week ago* (last edited 1 week ago)

The choice of algorithms usually comes down to your goals and your modeling assumptions. For example, if you think the data is drawn from a Gaussian distribution, then you would use mean squared error. If everything roughly lines up on a plane or different labels can be separated easily by a plane, then you could use a linear function and related tools for optimizing them.

We usually just make educated guesses by inspecting the data, but when you have messy real world data, that's hard to do correctly, so it makes more sense to just try a set of things to see what sticks. Note that this doesn't mean trying everything under the sun. You should still understand your problem well enough to restrict that set to a reasonable size.