https://github.com/sylvaincom/high-dimensional-statistics
[Python, R] My homeworks for the Statistics for high-dimensional data course of my MSc @ Mines Nancy
https://github.com/sylvaincom/high-dimensional-statistics
boosting boosting-algorithms lasso-regression ridge-regression
Last synced: about 1 year ago
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[Python, R] My homeworks for the Statistics for high-dimensional data course of my MSc @ Mines Nancy
- Host: GitHub
- URL: https://github.com/sylvaincom/high-dimensional-statistics
- Owner: sylvaincom
- Created: 2019-10-13T20:22:18.000Z (over 6 years ago)
- Default Branch: master
- Last Pushed: 2019-11-24T14:07:38.000Z (over 6 years ago)
- Last Synced: 2025-01-30T09:41:27.881Z (over 1 year ago)
- Topics: boosting, boosting-algorithms, lasso-regression, ridge-regression
- Language: Jupyter Notebook
- Homepage:
- Size: 1.2 MB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# High-dimensional statistics
My homeworks for the [Statistics for high-dimensional data](https://wikidocs.univ-lorraine.fr/display/minesnancyficm/GIMAS9AD+STATISTIQUE+EN+GRANDE+DIMENSION) course of my MSc @ Mines Nancy. I use R or Python, both in the form of Jupyter Notebooks.
The [boosting notebook](https://github.com/sylvaincom/high-dimensional-statistics/blob/master/boosting.ipynb) complements our report on boosting. It contains a few implementations of the methods that were studied. In particular, we compare several classifiers on several datasets (for classication). All functions (except XGBoost) and datasets come from scikit-learn.
This repository is mainly intented to my teacher, thus there are no further explanations for now. In particular, [tp3](https://github.com/sylvaincom/high-dimensional-statistics/blob/master/tp3.ipynb) is a homework assignment but I can not share the exercise statement for now.