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My data is not a linear data, so what do you suggest which model best to predict?
See plots, X is feature, Y is target
I did scale the feature, and just feel like confused since i got low r2 score always.
Any suggestion?
See plots, X is feature, Y is target
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Closed 6 days ago.
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I want to create below image distribution with n=10 in python code.
image
After searching on the Internet, the distribution most similar to this distribution is the binomial distribution, but the formula is different.
So, there are two things I want to ask you.
When n choose k, it should be written with nCk. Can I use n and k on the right side of C? (Even after searching for a combination, I couldn't find a case where it was written on the right side.)
Is there a distribution name for this formula?
thank you
After searching on the Internet, the distribution most similar to this distribution is the binomial distribution, but the formula is different.
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Closed 5 months ago.
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Is there any Hierarchical Agglomerative Clustering implementation (in Python) available that preserves the order of data points? For example, I want the output something like this.
(((seg1, seg2), (seg3, seg4)), seg5)
but not like this
(((seg1, seg5), (seg2, seg3)), seg4)
E.g., Actual output with existing implementation
Expected output (any implementation?)
Vijaya, from what I know, there is only one public library that does order preserving hierarchical clustering (ophac), but that will only return a trivial hierarchy if your data is totally ordered (which is the case with the sections of a book).
There is a theory that may offer a theoretical reply to your answer, but no industry-strength algorithms currently exist: https://arxiv.org/abs/2109.04266. I have an implementation of this theory that can deal with up to 20 elements, so if this is interesting, give me a hint, and I will share the code.
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Closed 1 year ago.
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I'm trying to get some approximation ratios for the Maximum Independent Set Problem and so I need some exact solutions !
I've found libraries written in C++ (i.e https://github.com/iPapatsoris/Maximum-Independent-Set)
but wondered if there were any directly in Python. I know of the `networkx' maximal indepedent set function but these are only approximations.
I realise it's far from the most efficient language to use but I'm only solving small Erdős–Rényi graphs (N<20).
In addition to this, are there any libraries that solve this for the weighted problem, where some nodes matter more than others?
This is the only python library I could find:
https://github.com/pchervi/Graph-Coloring/blob/master/Coloring_MWIS_heuristics.py
I haven't checked that it works correctly however.
I've been using KaMIS instead, which is a C++ implementation.
https://github.com/KarlsruheMIS/KaMIS
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Closed 2 years ago.
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I want to forecast upcoming total users on a daily basis within Python using a machine learning algorithm. Check the pattern below:
Looking at this graph, I was wondering if someone knows which forecasting method in Python I should use to predict?
Thanks!
If you have no additional data expect the user data over time which you have shown, the only thing you can do is try to find a function dependent on time which gives you a good approximation for that plot (ordinary curve fitting). I suppose that's not what you want.
To do a predection (which can be done not only by a machine learning approach), you need other data which is somehow correlated to the data you want to predict.
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Closed 9 years ago.
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I am intermediate in R and a beginner in Python. However my core abilities lie less in data analysis and more in programming and developing large software systems in teams, and I don't have time to become an expert in both.
Given the advances in the Python world in numpy, scipy, pandas, and its prevalence in data science and in general programming, I think I need to concentrate on Python (even though I enjoy R a lot), and accept that for some tasks I might be 75% as efficient, say, as I would be in R. I'd find this efficiency loss acceptable in order to be a master of one language rather than intermediate at both.
However I don't know enough about either language to really be sure of my facts. I would be very interested in hearing from anyone who is experienced in both R and Python and can say what would be the significant disadvantages, if any, of dropping R in favour of Python?
Edit 5: this question on stats.stackexchange is similar and has some great answers.
(Edits 3-4: reverted content/title to original question, which was closed. The original question attracted a lot of expert comment, my attempt to narrow the question to reopen it failed, and I'd prefer to have these comments below the original text they were commenting on.)