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 :



Follow on twitter : @unfoldds


Follow on Instagram : unfolddatascience


Watch python for data science playlist here:

Watch statistics and mathematics playlist here :

Watch End to End Implementation of a simple machine learning model in Python here:

[mosama]

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 🙂

[Sagar_Tachtode_]

You just nail the big concepts with a simple example.
Thank you. Keep it UP.

Grow fast and furious!!

[vishalrai]

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

[_proton]

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

[prakharagrawal]

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

[askpioneer]

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

[muthierry]

Great explanations .. thank you very Much.. Sir

[sunilsharanappa]

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

[nooreldali]

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

[GopiKumar-nyxx]

Useful information....nice presentation

[imranaziz]

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

[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.

[tejagunupudi]

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

[sudhavenugopal]

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

[Gilco]

You have explained the subject very well!!

[srinivaskrnagar]

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

[shivanshjayara]

@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

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

[nikhilgupta]

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???

[kirtisardana]

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 🤔?

[dimplechutani]

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 ?