I'm studying Data Science at MIT, you really can't imagine Aman how much "Unfold Data Science" is helping me, and a couple more channels, before I start any topic I like to tackle it first or just take a general idea, and you can't imagine how much your videos helped! Short, concise, and to the point! Thank you Aman 🙂

You just nail the big concepts with a simple example. Thank you. Keep it UP. Grow fast and furious!!

Please don't dislike, he is the bestest trainer, this shows that knowledge is power and maximum other KZfaq videos are replica of one another making small modifications with no proper concept keep it up, you are the best

wow what a teacher you are exceptional i think no one on youtube can teach like you in so easy and lucid way thank you sir

Excellent explanation in simple English. Keep up the good work Aman! Thanks!

Beauty of this lecture is very easy and elegant explanation in simple English. deadly combination.. Thank you Aman

my great teacher, thanks

wow the example about salary in decission tree was sooo good! hats off

Thank you

Superb explanation , Keep going and growing. Thanks a lot.

I like your simplicity in teaching , you made topics simple. great job aman.

Great Explanations Aman.

Bravo! Excellent exposition.

Excellent explanation in simple terms

Excellent explanation. Esp the details on what happens when on feature is not selected and how it helps other features to vote in. Probably this also leads to feature importance too.

You are a great teacher!

Dear Aman, thank you for your excellent explaination. As ai am a slow learner, I have a doubt from 11.25 mins. Is that the did advantages of Decision tree or Random Forest, because your video is the only source of my learning journey

#Interesting

excellent content

Well explained sir

Useful information....nice presentation

This is pretty good sir. got a lot of input from this video

well explanation!!

Excellent presentation and content in a simplified way and shortest time ! Kudos to you. Thank you

wpw!!! what a gift in teaching!!!

Great explanations .. thank you very Much.. Sir

Thank you Aman!

Super lecture, easy to understand, keep up the good work bro...

Hi Aman ...Till now your all videos are in order if following playlist from older to newer manner . Looks like now decision tree video should be part of this playlist after explaining ensemble and before random forest .... what do you think 🤔?

Good ..aman

Hi Aman . This is really great to see all the concepts in easy ,manner . Thanks for uploading it . I have a quick question , when we are testing our dataset on different decision trees then testing dataset will have all the N Columns and decision trees will have n1,n2,n3 columns then how it works ?

Very good explanation ❤

very great and clear

great video , simple and easy to understand , Thank you sir !

Hi Aman, While you say output of random forest is majority(suppose Y). Does that mean for all 300 inputs the prediction would be Y now. and for all test data the prediction would be Y only???

You have explained the subject very well!!

very helpful, keep up the good work !

Sir , very nice video. Do you also take paid course?

Hi Aman, thanks for your videos These are really informative and helpful I have one question I was asked by an interviewer When to use random forest instead of xgboost ?

Well explained, Thank you👍

I have a que. if we have 1000 of record data nd we build random forest and n_estimaters=10,then in each decision tree how many record will get train

Dear sir, are there are two methods of constructing random forest algorithms ?

finished watching

I have one doubt, in which scenario i choose decision tree ML over random forest, because it seems random forest is the best , then why should we use Decision Tree classifier

Sir how can we decide which catagory is to be taken as the root node of any decision tree when more than 2 catagory is given in data

for example if there are 500 decesion tress then it predicts 250 1 s and 250 0 s what the random forest will declares sir??

Can you make videos on linear regression and logistics regression.

Sir for binary classification if no of tree are even number say we have 6 tree out of which 3 is yes or 1 and rest 3 is 0 or No Then what should b output of our Random forest method yes or No or else??

I don't think the sample has to have less observations. We sample N times for N rows of data

What is pasting?

Is random forest only for predicting? I’m tying to see which features affect the income of taxi drivers in NYC. Can I use random forest for that?

How to choose number of samples

Hello Aman can you please explain what it the difference b/w random forest classifier & extra tree classifier?

if i have a more than 2 classes what to do

There is no doubt that RF is much better than Decision Tree, then why still Decision Tree still in use ?

Thank u

@10:22 You said that salary may not be part of further decision tree...(may not ) but what if salary is the only feature which has less entropy and high information gain. If it is so then i think in every decision tree root node will be salary only..... Or if it is taking different different rows and columns then i think it may happen that salary may not be always selected as a root node? i think i have question you also and answered my question by my own but you tell if im wrong then correct me please

sir, just a small doubt what are these decision trees in random forest classifier made of like do they have other classifier such as ann, logistic regression, svm and other types in them? is it so or something else

Is there a proof of random forest’s accuracy as an algorithm? Thanks

are you from Rajistan?