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statsmodels / statsmodels

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ENH: Improvements to new ARIMA-type estimators

11
ChadFulton
ChadFulton commented Sep 11, 2019

Collection of follow-ups to #5827. These can/should be broken out into individual PRs. Many are relatively straightforward and would make a good first PR.

General

  • Documentation (none was added in original PR).
  • Release notes.
  • Example notebook.
  • Double-check how sm.tsa.arima.ARIMA works with fix_params (it should fail except when the fit method is statespace
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comp-tsa type-enh good first issue
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danielhanchen / hyperlearn

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**IMPORTANT: On Contributing to HyperLearn + Note to Contributors

5
danielhanchen
danielhanchen commented Aug 29, 2018

Hey Contributor!

Thanks for checking out HyperLearn!! Super appreciate it.

Since the package is new (only started like August 27th)..., Issues are the best place to start helping out, and or check out the Projects tab. There's a whole list of stuff I envisioned to complete.

Also, if you have a NEW idea: please post an issue and label it new enhancement.

In terms of priorities, I wanted

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help wanted good first issue

maykulkarni / Machine-Learning-Notebooks

Star 276

Machine Learning notebooks for refreshing concepts.

python machine-learning natural-language-processing reinforcement-learning deep-learning machine-learning-algorithms neural-networks deep-learning-algorithms dimensionality-reduction python-machine-learning data-processing regression-models deep-learning-tutorial data-science-notebook model-evaluation classification-trees clustering-methods machine-learning-tutorials
  • Updated Aug 24, 2021
  • Jupyter Notebook

NishkarshRaj / 100DaysofMLCode

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Star 214
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dominance-analysis / dominance-analysis

Star 106

This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.

feature-selection logistic-regression feature-engineering regression-models predictor multiple-regression feature-importance classification-model relative-importance dominance-analysis r-square shapley-value keydrivers dominance dominance-statistics predictor-importance pseudo-r-square
  • Updated Jun 17, 2021
  • Python
notebooks

differential-machine-learning / notebooks

Star 79

Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.

machine-learning deep-learning tensorflow automatic-differentiation pricing derivatives quantitative-finance notebooks regression-models backpropagation risk-management computational-finance aad risk-magazine
  • Updated Nov 26, 2021
  • Jupyter Notebook

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