Data Science - Part V - Decision Trees & Random Forests

[@nayakool]

Excellent!! It really helps me solidify some of the concepts used in DT and its practical applications !!

[@DerekKaneDataScience]

+rakshith nayak Thank you and I am glad that you found some value here. Keep it up!!!

[@graciehan8948]

Learned a lot today. Thank you, Derek!

[@arasuthiru8548]

Very good overview and examples a very useful.

[@mojazzz284]

While I appreciate all the efforts in putting up this presentation, I hopefully wish that you explained the methodology of all these concepts with some practical explanations / algorithms to help us better understanding on the applications.

[@bhavanichatrathi7435]

nice explanation,can you please share the links to data sets

[@m.anisulkarim7819]

Excellent. Really helpful. It would be much appreciated if you provide a link for data source and code.

[@ShubhamAshokGandhi]

Hi @Derek Kane. Thanks for the video. It was helpful. Can you share the dataset please?

[@cragnosliw4685]

hi Derek, can you pls share the dataset and R scripts?

[@fatmaghorbel6579]

could you apply an implementation of algorithm of CART with python or any other language based on an easy example ??

[@sourabhiitg1]

Derek, your presentations are very helpful. Would it be possible to give a link for the various datasets that you are using? Some of them are in R but e.g. the phone company 'churning' dataset is not available. If I could practice on the real datasets that would be a better learning experience

[@DerekKaneDataScience]

+sourabh sriom Absolutely... Please pm me and I will get you setup on my dropbox account which has the R scripts/ datasets. I will be creating a GitHub account eventually to centralize all of this content but hopefully this will work in the meantime. Good luck and let me know what I can do to help.

[@rjronquillo4488]

Very nice video! Keep up the great work!

[@jensharbers5620]

Is the green in 29:14 not the diagonal and red the off diagonal? Like in matrices?

[@99chintu]

Can you please share a link to the datasets..Thanks

[@DerekKaneDataScience]

+99chintu please pm me and I will get you setup on my dropbox account which has the R scripts/ datasets. I will be creating a GitHub account eventually to centralize all of this content but hopefully this will work in the meantime. Good luck.

[@hannachuray4614]

Hi Derek, sorry for asking, but did you create a GitHub account for the content? I am very interesting to see the R script, if it is possible. Thank you

[@hannachuray4614]

no, sorry

[@LBizKid04]

I just sent him a PM, hopefully he'll get back to me :)

[@hunhwasong8800]

Thank you for the video! such a useful lecture.

[@DerekKaneDataScience]

+hunhwa song You are very welcome.

[@lariksonfar]

please re-explain at 9:09 information gain...this entropy stuff is highly unclear as to how increase in disorder means its a useful splitting criteria...and what does it mean to just "increase in disorder"? are you transforming the data in some way to test something?... overall great video

[@aueret]

first think of entropy being a measure for uncertainty and secondly imagine a decision tree that at some node (representing a specific feature) divides a data set into subsets. The "cleaner" these subsets are (in terms of containing individuals belonging to a certain class when doing a classification problem) the better the feature is. The disorder you are talking about is actually not a disorder but the division ability of a feature in regard to the classes. hopefully understood your problem correctly and that this helps.

[@gregweston20]

Great video

[@rezadarvish8116]

Thanks a lot very indepth talk

[@test-mm7bv]

not very helpful. need far more mechanical details. how are purity metrics used? how to combine multiple trees? how to determine variable importance.

[@mojazzz284]

couldn't agree more with you... basically he just kinda of narrated the text on the slides most of the time... not much help for newbie like me

[@iGatflow]

Somehow he hasn't said a word about actually building a DT... 'We will build a CART model' and the next sentence 'We built a CART model' ? Really? Was it magic? If you're looking for anything practical, skip this video. If you're looking for theory, the video is still too thin

[@DerekKaneDataScience]

Hi Kole. I'm sorry that this video lecture did not give you what you are looking for and I appreciate the feedback. It is somewhat difficult to strike a good balance between theory/practical applications/algorithm optimization / code and my intention was to expose the viewer to the basic mechanics where they can build off in the direction that best suits their needs.

[@yrnagaraju6685]

Poor quality of audio recording.

[@Meenakshi2108]

bad vdo and audio quality