Random Forest Algorithm Clearly Explained!
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- čas přidán 20. 04. 2021
- Here, I've explained the Random Forest Algorithm with visualizations. You'll also learn why the random forest is more robust than decision trees.
#machinelearning #datascience
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Easily the best video on Random Forests I've seen
Thank you!!
Just came across your channel and i must say you deserve a lot of accolades for how much effort you put into visualizing these concepts and explaining the motivation behind everything so well. Good job really. Not many like you out here
Well done! I've been reading/watching tutorials on this subject ad nauseam for the past week and yours was the first to clearly explain it. Will definitely be watching more of your videos.
Not only a very well-explained video, but aesthetically superb too; the diagrams, the music when the trees are being created - brilliant video! Well done!
Hey, I really like the fact that you tend to justify why certain concepts are used the way they are! Hoping to see more fundamental machine learning concepts covered in the future!
That's exactly my goal!
Exactly!
I totally agree really helpful, thank you for the nice videos
Unbelievable clarity and simplicity. Hallmark of someone who has truly understood in depth and genuinely wishes to share😊
Love your animations, they make it so easy to understand. Best that I have seen so far!
As someone who makes videos on machine learning, I'll say this is an excellent explanation. I like how the algorithm is explained verbally with a visual example. Also, you explain the motivation for the choices of algorithm as you come across them. Variance reduction is key! Very nice - keep it up!
Thanks mate! 😄
I just watched your distribution video and enjoyed it a lot...great work!
@@NormalizedNerd Thank you! Much appreciated :)
variance can be reduced by increasing the no of estimators or trees and by decreasing the no of row sample and column samples for each tree
This is amazing... I spent a lot of time searching for the right channel to understand machine learning, still there were complexities understanding, but this is simple and well explained... Thanks and keep posting videos!!
Thanks so much! This is so helpful! I’m considering employing RF for diagnosis classification in neuro-imaging, and this video made me understand that RF may be the right fit for my task!
I've done a few machine learning courses on CZcams and LinkedIn and none of them give a good explanation for bagging and I struggled with why and how you would logically aggregate over many models with different parameters
and the feasibility of the application of such models.
After watching this, I see a clearer picture.
Thank you
I've been normalized
;)
I am sending you much appreciation, talented stranger! You earned my like and subscription. I am currently getting into programming / GIS and I am very happy to have stumbled across your channel!
This was wonderful . Very short, to-the-point and covers all the necessary concepts. I think i have a clear understanding now.
Amazing video! I learned a lot on how this works. Will you or do you have videos that talk about what kind of real application scenarios are the best to use the random forests model and why.
These videos are the best machine learning explanations I've come across anywhere so far, thanks heaps !
I have watched several wideos and read a bunch of articles but I still don't know how a radom forest works until I found your video. Thank you!
Hey, your explanation about the maths behind the algorithms with pretty visualisation is awesome. Please upload more videos for other Algorithms, So that begginers like me can enjoy the learning.
I had no idea about what is random forrest before watching it. This 8 minuts talk helped me alot! Thank you!
I was struggling with this concept, but your video was so informative and clearly explained the idea behind it. Instantly subscribed to your channel. Thank you for sharing your great work.
Glad it was helpful!
OMG.....Really thank u for this ..... i literally haven't seen such an amazing Explanation on Random Forest.... it really helped me to get a perfectly clear picture about this Algo....
This is really well-detailed explanation! Thank you very much for explaining mathematical part so easily.
Genuinely the most clear video I've yet to see on Random Forest, I can't believe I finally understand !!
Hi man, first of all your videos are amazing. It is nice to see, that you can describe such complex topics so easy!!
Do you have the name of the paper, which investigate that the number of selected features should be near to the log or square root?
This channel is so Underrated!!! This guy is explaining in the simplest way!!!
Excellent video! Very clear explanation and the animation was really easy to follow.
Very helpful video! I have no idea of Machine Learning algorithms but am required to write a term paper on it and your videos help a lot!
One of the best video that I've come across that explains random forest so easily. 👏
Hey, Really superb videos with a clear explanation & the graphical represntation will help to understand easily, Thanks for the videos and expecting more in future.
Awesome job at explaining the algorithm clearly, very helpfull. Thanks a lot !
Sir, your videos are phenomenal. Extremely thorough and very informative. I wish you all the best in your future endeavors!
Excellent use of Manim (by 3 blue one brown). Thanks for the great explanation!
Dude I have to say that your videos are really of the best I have watched!! Thank you so much for making those!!
So glad to hear that!
Wow....amazingly well explained. Thank you so much for creating this wonderful video.
Thank you so much for this video, great explanation and really well executed, kudos!
Nice and clear explanation with animation and reasoning. keep it up!
I love your explanations, you are the best to teach these complex concepts
I liked your mind. You ask philosophical questions and explain those. This is very good learning and teaching method.
Thank you, this video really helped me understand random forests
Enjoyed and appreciated this so much. Clear to the point. Thank you so much!
You are amazing.Literally whenever I search for a ml algorithm on youtube your channel pops-up.Thank you for your content🤗
You're an excellent teacher & this video was amazing 👏🏾 thank you 😊
Amazing graphics and clear explanation. Thank you!
the explanation is clear and thorough, love it!
Favorite Random forest video yet!! Thank you Normalized Nerd!!
Wow, thanks!
Hi, I accidentally found your CZcams channel and then noticed it is very informative and helpful! Thank you so much for the high-quality content. Please we are looking for more ML algorithms from scratch specially the ensemble algorithms, we will be so grateful if you make videos on those, too!
Great to hear that. I'm planning to make more such videos.
@@NormalizedNerd But you haven't🥲
Thanks for your video, it's straightward and very dedicatedly prepared!
Thank you for the video. The best explanation I’ve seen so far
Hi, The explanation is very nice. One thing i am missing is how the tree is deciding which feature to select as root node and in case of continuous variable, what value the root node should check to make the decision? If this is explained, then it will be perfect in my opinion. Overall Good work. Keep it up.
best best best explanation !! And the visuals take the explanations to another level !
Best video on random forest. Very well explained. Thanks!!!
It so soothing bro the piono in the background and keep it up bro we really like your videos amazing
Very good explanation and very good your animation to explain it! Thanks NN, subscribed!
The visualization made it easy to understand! Loved it.
love the animation and clear explanations. for classification though, would it make more sense to make sure to use an odd number of trees so that for majority voting you won't have a case of equal number of positive predicted classes vs negative predicted classes? awesome stuff keep it up!
it does not matter in practice, where we use 500 or 2000 trees. Tiebreaking will not make a big difference
Thank you, this really helped me understand random forests easily
Glad to hear that!
Concise and precise, thank you very much! Here, you have a new suscriber
Thank you for the high quality video and explanations
Really, a nice video, piano music while creating the trees, really nice, congrats for your dedication, thanks for sharing your knowledge
This is actually pretty good, nice job!
this was very well explained and simple to understand
this is a really good quick summary of how random forest work. A quick question- during boostrapping, why we do random sampling with replacement, rather than random sampling without replacement? is there any research conducted to demonstrate one is better than the other?
if your bootstrap generated datasets are the same size as the input, then every sample by selecting without replacement would just be a permutation of the original data. with replacement, the proportion of unique entries tends to 1-1/e.
Excellent video, thank you! I got one small comment. In the original algorithm, a subset of features is selected at every node of the tree. So every tree gets the total set of features, but only a random subset of these features is used at every node.
Nice, clear explanation. Many thanks!
Great job bro, your channel is under-rated.
Marvelously explained, thank you so much
I like how you Connect ML terminology with Concepts,
Underated Channel
Thanks man!
Great explanation. Keep up the good work!
Thanks, will do!
Thanks a lot for this wonderful presentation.
When you use the data point example and apply it to the trees, how are those data points entered to obtain the output e.g., 1 from model 1
Well explained explanation, and great visuals!
Thanks a lot!
You're very welcome!
great video, love the music btw :D
Thanks for the video, very clear and detailed!
I am thankful to you for providing such high quality content. Bro, by mistake you have written x2 and x1 two times in last two trees.
Great explanation. Thank you!!
Amin the medical field, not big fan of stats, but need this knowledge for my research. You did a great job in explaining the concept. Big fan!!
Hey Normalized Nerd you are the best! You explained these concepts better than my professors.
Excellent video! Please make a video on Boosting and BART ( Bayesian Additive Regression Trees)
Excellent video on the Random Forest algorithm!
This is amazing content, thank you !
This should be on the top of search results for what is a "Random Forest".... great job, well explained.
I also think so :P
Man ! This is amazing 🔥 and the video length is also short ❤️✨
Thanks for this. It was really helpful!
Your explanations are so well and interesting... ❤❤❤
The best concise explanation!
Such a good explanation - thank you
Thanks for helping out, very clear!
Wonderful explanation! Thank you so much!
Loved the content!
Thanks for sharing this content.
Great video!!!
:D
Amazing explanation! Thanks so much :)
clear explanation and clear visualization, it didn't even feel like learning.
This is the most helpful machine learning video I have ever seen!
Hey can you please cover Markov jump too..I really loved your Markov chain series
super clear. loved it.
Amazing illustrations👏
Great content!! Congrats
I think there is a little bit of miss information(as I've watched some other videos like statquest and read some articles) we do not use the same randomly selected subset of features through out the tree from root node to last decision node but we randomly select a subset of features at each decision node to decrease the correlation between the decision trees and make them more robust.
Yes! Exactly. I was confused about the same and this video just fueled my confusion.
Awesome Video! Can you make one of these but for regression Trees?
I'm so glad I found your channel ! I wanna see more videos ! :D Do you have a tipeee/patreon ?
Great to hear! Definitely more videos are coming.
I'm planning to start my Patreon soon. Currently, I have a page on buymeacoffee:
www.buymeacoffee.com/normalizednerd
Thank you for nice lecture ☺ feels like love with decision tree !
You're welcome 😊