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LASSO regression is a useful technique for building sparse models and aiding in variable selection. Suggest we implement it as an alternative to the existing methods.
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> Machine learning focuses on prediction, based on known properties learned from the training data.
- In addition to retail, in finance banks analyze their past data to build models to use in credit ap…
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### pycaret version checks
- [X] I have checked that this issue has not already been reported [here](https://github.com/pycaret/pycaret/issues).
- [X] I have confirmed this bug exists on the [la…
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```
m,n,p = 100,20,.1
A = sprand(m,n,p)
x0 = Base.shmem_randn(n)
b = A*x0
rho = 1
quiet = false
maxiters = 100
params = Params(rho,quiet,maxiters)
z = nnlsq(A,b; params=params)
```
Running the above…
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Below are some places to look at to see what kinds of topics should be covered. Comment below for what you think should be covered in the series of videos. Topics can range from simple to complex.
…
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Should GLMsingle also provide some help to the user to, e.g., compute condition means? Like, perhaps indexing vectors based on the contents of the design matrix?
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Hi, I used the tutorial notebook and was not able to reproduce the benchmark results "HEST-Benchmark results (08.30.24)" posted on the main page. For example, the ridge regression results are consiste…
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Hi @dllussier!
My team and I have come up with a variety of classifier models from [this article](https://github.com/orgs/brainhack-school2020/teams/url) and I was wondering if you had some suggesti…
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New jupyter notebook from this [link](https://www.kaggle.com/janniskueck/pm3-notebook-newdata).