How Random Forest Work|How Random Forest Algorithm Works|Random Forest Machine Learning

How Random Forest Work|How Random Forest Algorithm Works|Random Forest Machine Learning
#RandomForest #RandomForestMachinelearning #UnfoldDataScience
HI,
My name is Aman and I am a Data Scientist.
About this video:
Want to learn why Random Forests are one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning? What this video tutorial explaining the basics of Random Forests.
Random forest algorithm is a one of the most popular and most powerful supervised Machine Learning algorithm in Machine Learning that is capable of performing both regression and classification tasks. As the name suggest, this algorithm creates the forest with a number of decision trees.
In general, the more trees in the forest the more robust the prediction. In the same way in the random forest classifier, the higher the number of trees in the forest gives the high accuracy results.
To model multiple decision trees to create the forest you are not going to use the same method of constructing the decision with information gain or gini index approach, amongst other algorithms. If you are not aware of the concepts of decision tree classifier, Please check out my videos here on Decision Tree CART for Machine learning. You will need to know how the decision tree classifier works before you can learn the working nature of the random forest algorithm.
About Unfold Data science: This channel is to help people understand basics of data science through simple examples in easy way. Anybody without having prior knowledge of computer programming or statistics or machine learning and artificial intelligence can get an understanding of data science at high level through this channel. The videos uploaded will not be very technical in nature and hence it can be easily grasped by viewers from different background as well.
Join Facebook group :
groups/41022...
Follow on medium : / amanrai77
Follow on quora: www.quora.com/profile/Aman-Ku...
Follow on twitter : @unfoldds
Get connected on LinkedIn : / aman-kumar-b4881440
Follow on Instagram : unfolddatascience
Watch Introduction to Data Science full playlist here : • Data Science In 15 Min...
Watch python for data science playlist here:
• Python Basics For Data...
Watch statistics and mathematics playlist here :
• Measures of Central Te...
Watch End to End Implementation of a simple machine learning model in Python here:
• How Does Machine Learn...
Have question for me? Ask me here : docs.google.com/forms/d/1ccgl...

[@mosama22]

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 🙂

[@muthierry1]

Great explanations .. thank you very Much.. Sir

[@askpioneer]

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

[@Sagar_Tachtode_777]

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

[@UnfoldDataScience]

My pleasure Sagar.

[@GopiKumar-ny3xx]

Useful information....nice presentation

[@UnfoldDataScience]

Thank you.

[@spicytuna08]

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

[@vishalrai2859]

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

[@UnfoldDataScience]

So nice of you Vishal.

[@azingo2313]

Very good explanation ❤

[@nooreldali7432]

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

[@UnfoldDataScience]

Your comment means a lot to me. Keep watching

[@eyobsolomon4663]

#Interesting

[@UnfoldDataScience]

Thanks a lot.

[@tejagunupudi5318]

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

[@_proton5]

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

[@UnfoldDataScience]

Welcome Harshal.

[@sunilsharanappa7721]

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

[@UnfoldDataScience]

Thanks again Sunil :)

[@prakharagrawal4011]

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

[@UnfoldDataScience]

Welcome Prakhar.

[@alkashie9174]

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

[@UnfoldDataScience]

You are welcome. Keep watching:)

[@sangrammishra9923]

excellent content

[@UnfoldDataScience]

Thanks Sangram.

[@imranaziz5856]

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

[@UnfoldDataScience]

Thanks Imran.

[@mehdimediouni9659]

very helpful, keep up the good work !

[@UnfoldDataScience]

Thanks Mehdi, will do! Stay Safe. Tc.

[@user-ur2en1zq4f]

my great teacher, thanks

[@UnfoldDataScience]

You are welcome!

[@nikhildesai2460]

Great Explanations Aman.

[@UnfoldDataScience]

Thanks Nikhil

[@syedkamran6249]

Well explained sir

[@UnfoldDataScience]

Thanks Syed :)

[@santhoshkumar-dd6xq]

Excellent explanation in simple terms

[@UnfoldDataScience]

Thanks Santhosh.

[@Gilco333]

You have explained the subject very well!!

[@UnfoldDataScience]

Thanks a lot for the feedback.

[@sandipansarkar9211]

finished watching

[@srinivaskrnagar4029]

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

[@UnfoldDataScience]

Keep watching Srinivas.

[@soheilaahmadi4807]

very great and clear

[@UnfoldDataScience]

Glad it was helpful!

[@uchennanwosu5327]

Bravo! Excellent exposition.

[@UnfoldDataScience]

Thanks a lot

[@adieu_bae]

You are a great teacher!

[@UnfoldDataScience]

Thanks a lot.

[@amoldhumane8277]

Thank you Aman!

[@UnfoldDataScience]

Welcome!

[@smegala3815]

Thank you

[@UnfoldDataScience]

You're welcome

[@archanamohapatra7589]

Well explained, Thank you👍

[@UnfoldDataScience]

Glad it was helpful Archana.

[@bangarrajumuppidu8354]

well explanation!!

[@UnfoldDataScience]

Glad it was helpful!

[@Birdsneverfly]

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.

[@UnfoldDataScience]

Thanks for watching

[@seetharaman5262]

if i have a more than 2 classes what to do

[@rusiraliyanage6643]

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

[@sudhavenugopal3726]

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

[@UnfoldDataScience]

Disadvantage of Decision tree - Overfitting
Disadvantage of Random Forest - Resource intensive algorithm

[@kalam_indian]

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

[@UnfoldDataScience]

Thanks a lot. Your words are precious for me.

[@gangulreddy7918]

Thank u

[@UnfoldDataScience]

Thank you.

[@jailata3822]

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

[@dimplechutani2768]

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 ?

[@UnfoldDataScience]

Very good question - its not a parametric model so it does not matter.

[@sadhnarai8757]

Good ..aman

[@rai183]

Thank you.

[@UnfoldDataScience]

Thank you :)

[@praveensingh9688]

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

[@UnfoldDataScience]

Decision tree.

[@kirtisardana8479]

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 🤔?

[@UnfoldDataScience]

Thanks for feedback Kirti. Let me check if I can rearrange. Happy learning. tc

[@nikhilgupta4859]

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???

[@itsarsh4421]

Its not like that you taking it wrong. Not all 300 data points will predict Yes. It shuffle data point row and column wise(not all 300 data points but 2/3 of the data). Its like if row no.1 given in bag1 and 2 other bags also with the corresponding to other feature. And bag1 giving output "Yes" and other 2 bags giving output "No" . Then it play a democratic rule which is every individual have same weightage and right to vote.
So in this case the output will be "NO"

[@sagarmestry5514]

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

[@UnfoldDataScience]

Hi Sager, for each feature , a random value is selected for the split in case of extra tree. I will explain in more detail in a video. Thanks you

[@arko1383]

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 ?

[@UnfoldDataScience]

Xgboost needs more server capacity on large data, random forest you get variablr importance, many more points to consider as well. This is a short answer

[@ravanshyam7653]

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??

[@UnfoldDataScience]

Highly unlikely scenario.

[@krishna7440]

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??

[@usbabu]

I think it just desides by tossing a coin🤔.

[@askpioneer]

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

[@UnfoldDataScience]

Normally we use random forest or boosting directly. No decision tree

[@mehtashyam5196]

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

[@UnfoldDataScience]

Could you rephrase your question please?

[@shekharkumar1902]

What is pasting?

[@rafibasha4145]

How to choose number of samples

[@user-px5of1bi2g]

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?

[@tpennyhealth5861]

Ml generally is for predicting. Type of car, age of car, hours spent on the wheel, time of the day driver likes to work, economy of the city, and also drivers rating from other users

[@nidhichourasiya2743]

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

[@UnfoldDataScience]

Good question Nidhi.
There are two important parameters. One is "bootstrap" And other is "max_sample". For taking subset of data in each tree, you must say " Bootstrap " = True. By default it's True in python sklearn.
Coming to "max_sample", if you say " none"(default), all records go in all trees.
If you say a integer, those many rows.
If you say a decimal, that percent of total no of rows.

[@nidhichourasiya2743]

@@UnfoldDataScience tnx sir

[@shivanshjayara6372]

@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

[@UnfoldDataScience]

Hi Shivansh, All the columns will not be selected in every tree. Hence, its possible that "Salary" is not part of few trees hence there is no question of it being root node.

[@shivanshsingh5555]

Ok u mean to say that in randome forest all the columns are not get selected at once for all the decision tree...columns gets selected randomly?

[@UnfoldDataScience]

Yes absolutely.

[@vinijcobatgamildotcom]

Can you make videos on linear regression and logistics regression.

[@UnfoldDataScience]

Hi Oyster, These videos are already there on my channel. Please find link below:
ua-cam.com/video/8PFt4Jin7B0/v-deo.html

[@tpennyhealth5861]

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

[@UnfoldDataScience]

Both options are there

[@HtS643KyS6555GxQ3edA]

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

[@HtS643KyS6555GxQ3edA]

Breiman 2001 has it

[@UnfoldDataScience]

sklearn will give that.

[@ajaykushwaha-je6mw]

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

[@UnfoldDataScience]

Not in use mostly.

[@geekyprogrammer4831]

are you from Rajistan?

[@UnfoldDataScience]

No Sir.

[@geekyprogrammer4831]

@@UnfoldDataScience I should be the one to call you Sir lol. Regardless from where you are, your Data Science content is gold. The way you boil down and explain complex concepts in very simple English is really mind blowing. In shaa Allah planning to see all of your videos and extract maximum information from your channel.