Open prathimacode-hub opened 2 years ago
I would like to work on this issue. Name: Biswadeep Serial No.: 266 Type: Video(Presentation) Approach: Will discuss intro to cross validation, various cross validation techniques like K-Fold, Leave One Out, Stratified K Fold , comparison of various techniques and implementation with code examples.
Issue assigned to @RBiswa787 for Video.
@prathimacode-hub Ma'am, Can I do the documentation for it? Vishaka Saxena Serial No: 901 Batch: 20 Issue Number: 422 Type: Documentation Approach: Introduction of Cross-Validation? Overfitting & Unfitting Various validation strategies Tips & Tricks
I would like to work on this. Apoorv Yadav Type: Documentation Batch: 15 Issue: 422 Approach: what is cross validation technique, how it works, how features impact the overall performance, useful features and not important Decide powerful preventive measures against overfitting and underfitting
I would like to work on this. Ankur Das Type: Documentation Batch: 4 Issue: 109 Approach: What is Validation? What is Cross Vlidation? Importance How it works How it can be implemented using python Paramaters abd Tuning Conclusion
@prathimacode-hub , Mam can you please assign me this issue , I would like to work on this. ( Video or audio ) M. Ashish Reddy Batch - 5 approach: a detailed explanation along with examples
You can work on for audio. If it is ok I shall assign @ashish-reddy-20-08
Issue assigned to @Vishaka830 for documentation.
Issue assigned to @ashish-reddy-20-08 for audio contribution
@prathimacode-hub , Can you please evaluate my PR mam PR LINK: https://github.com/girlscript/winter-of-contributing/pull/3625
Welcome to 'DSWP' Team, good to see you here
This issue will helps readers in giving all the guidance that one needs to learn about Cross Validation Techniques. Tutorial to Cross Validation Techniques and how it's applied using sample code.
To get assigned to this issue, add your serial numbers mentioned in the spreadsheet of "Data Science with Python", the approach one would follow and choice you prefer (Documentation, Audio, Video). You can go with all three or any number of options you're interested to work on.
If you had referred any resources, add them up in "DS Resources". Similarly if you had used datasets, include them in "DS Datasets".
Domain : Machine Learning
Mentors Assigned : Peehu Saxena / Jivitesh Jain
Points to Note :
The issues will be assigned on a first come first serve basis, 1 Issue == 1 PR.
"Issue Title" and "PR Title should be the same. Include issue number along with it.
Changes should be made inside the Datascience_With_Python/ directory & Datascience_With_Python branch.
Follow Contributing Guidelines & Code of Conduct before start Contributing.
This issue is only for 'GWOC' contributors of 'DSWP' domain.
All the best. Enjoy your open source journey ahead. 😎