Random Forest Regression And Classification Indepth Intuition In Hindi

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Random forests or random decision forests is an ensemble learning method for classification, regression and other tasks that operates by constructing a multitude of decision trees at training time. For classification tasks, the output of the random forest is the class selected by most trees.
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Опубликовано:

11 июл 2022

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Комментарии : 40

@kunikakhobragade6953 Год назад

sir aapne bohot achhese samjhaya each n every point in detail...tysm sir

@HarshalGarud Год назад

Excellent level of teaching

@satyendrajain2120 Год назад

your explanation part is too good..thanku you so much sir

@samarpratapsingh3088 Год назад

Ek no. explanation

@shorttube5888 2 года назад

Sir I am following your both channel hindi and english but I will join you on inuron also after this month I hope you will help in my data science carrer.

@subratpatra4380 2 года назад

Hi Sir, Thanks for the video...please continue upload the videos, you have uploaded this video after some gap :(

@Mani_Ratnam 9 месяцев назад

Gurudev ,jai ho

@pratiksonawale1997 Год назад

Amazing explanation sir.....

@princyshrivastava6360 10 месяцев назад

So nicely explained

@manalipareguha8439 Год назад

Very nice explanation👌👌

@pranavladhe7 Год назад

plz upload ADABOOST, GRADIENT-BOOST, XG-BOOST much needed videos...

@_SanikaJadhav 7 месяцев назад

Thank you❤

@zainknbehram4225 Месяц назад

great

@PrinceKumar-zh3nt Год назад

sir you are great

@hamzakhalidbaig5914 5 месяцев назад

Jo bhi idhar hai ..... kuch mahino me kadak placement lene wala hai ... :)

@AbdullahAbdullah-jc4uf Год назад

Nice explanation sir 😁

@anjanikumari-vj1cc Год назад

Owesome

@utkarshvijay6467 2 месяца назад

Nice presentation. Can I use the exact diagram for my work?

@krishnabhutada3983 2 года назад

But Krish, when we say row sampling and column sampling we have mandatory choose target(dependent feature) ?Otherwise how would each DT predict the output and when we combine all as a voting classifier or in regression (average)...That's my doubt please clear it...

@zaafirc369 2 года назад

Random forest is made up of a number of decision trees. Each decision tree in the Random forest is built on a subset of the dataset which i like to call "mini dataset". This "mini dataset" is a random selection of rows and random selection of the features. This "mini dataset" will of course include the dependent feature. It is only the independent features that are randomly selected. for example, if you had a dataset of 1000 rows and 5 columns(x1,x2,x3,x4,y) an example of a "mini dataset" could be : rows 1 to 300 and columns (x1.x2,y) Note: as an example i have taken rows 1 to 300. But in reality, the subset of rows and columns are randomly selected.

@krishnabhutada3983 2 года назад

@@zaafirc369 Thanks for your reply!

@rakeshbind8288 Год назад

training is good I'm learning many things from scratch it is helping a lot but for freshers, today's problem is how to get an interview I can train myself with your help with your videos but how do get an interview where I can apply If I have 0 experience in the technical domain (I do have 4+ year's of experience in non-technical domain and degree in BE - CSE, pass out 2019), I applied on multiple platforms but there is no luck so far will appreciate if you can share a video related to this issue as well I want to switch my career from Technical Recruiter to Data Scientist

@LuckySinghShekhawat 11 месяцев назад

Complete 2-3 projects and connect with your friends who have experience in this field and ask them to teach you their project in detail and then fake your resume with 2 yrs of relevant experience. If you won't fake it, then there are very rare chances that someone will consider you as they will always prefer a fresher over you since both have the same knowledge , according to them. Once you work in that organization for an year, switch again and now you know how things actually work in this field and you can get a lot of calls. P.S.- Currently, market is down, so that can also be a reason for not getting a call. Hope this helps :)

@hades840 4 месяца назад

is it kind of cross validation technique ?

@vinaykatewa6529 3 дня назад

Can someone provide me the link to the lecture note (from the board that krish is writing on)

@ravidawade5178 Год назад

how many decision tree are there in random forest

@Priyangshu_Majumder_18 7 месяцев назад

@riteshbisht94 7 месяцев назад

Depends on the situation

@Yzyou11 4 месяца назад

6:59 In row sampling dataset size of d==d' , right?. It's not d` less than d. Each bootstrap copy has the same size as the original training data

@knowledgeji6449 2 года назад

how will decide thta how many decision tree will make ?

@sudhirnanaware1944 2 года назад

Krish...sorry to say but I think you missed to explain Out of Bag error and data selection techniques for random forest..like if regression is there then total no of variables/3 and if classification is there then square route of total no of variables.. I am sorry but I just feel this is missing hence I suggested.. Thanks for your hardwork for data science community

@krishnaikhindi Год назад

Check the recent video I have already explained

@sudhirnanaware1944 Год назад

@@krishnaikhindi Thank you