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1 year agoAlso where to ask for help is important
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Joined 1 year ago
Cake day: October 27th, 2023
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Probably you wrongly tiped reddit.com instead of chat.openai.com in your browser bar
My dude, as always, the answer is: it depends on what you need. Without any details, the only possible answer is pick one and see if it works for you.
This is not stackoverflow!
r/learnmachinelearning
Have you read the license? Have you discussed it with someone at work? Have you searched on google? Have you tried asking chatgpt? Have you done any kind of research before asking?
r/learnmachinelearning
r/mlquestions
r/localllama
r/learnmachinelearning
r/mlquestions
That can be informative, but as I was saying, you have to limit the function space to those compatible with your hypothesis.
I repeat my question in a clearer way: do you know (or have a guess of) what the function would look like after x=60?
Since you mentioned the rate of change, have you ever plotted the numerical derivative of this function? Maybe it’s shape has a recognizable shape that could help you in identifying the right class
The issue here is that you want to extrapolate values outside of the training set (for x>60). You can even get to 0 error, R2=1 on the training data, but it would be meaningless, because you are going to predict outside of this range. If you don’t have data for the range that interests you the best thing you could do is to rely on domain knowledge.
For example, if you have reason to believe that the function is going to approach an asymptote, you can exploit this knowledge by limiting the class of fitting functions to e.g. parametric sigmoids.
Or if you know that the process you are modeling has a specific functional type, like logarithmic or squate root, then limit the function space accordingly.
If you have any other kind of knowledge about your function, it could be used as a prior distribution in a bayesian approach, like bayesian regression or gaussian process
Bottom line is, there is no magic button “make it work” i ml/statistical modeling, you have to embed your domain knowledge in. The modeling process is not a blind one.
r/learnmachinelearning
r/learnmachinelearning
Have you tried reading a ML book instead?
Markov chains or HMM
That’s basic linear algebra