Random Forest in Machine Learning: Easy Explanation for Data Science Interviews

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Random Forest is one of the most useful pragmatic algorithms for fast, simple, flexible predictive modeling. In this video, I dive into how Random Forest works, how you can use it to reduce variance, what makes it “random,” and the most common pros and cons associated with using this method.

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====================
Contents of this video:
====================
00:00 Introduction
01:09 What Is Random Forest?
02:10 How Random Forest Works
03:53 Why Is Random Forest Random?
04:20 Random Forest vs. Bagging
04:57 Hyperparameters
06:18 Variance Reduction
09:04 Pros and Cons of Random Forest
  1. Emma Ding
  2. Data Science
  3. Data Science Interview
  4. Emma Ding
  5. Data Interview Pro
  6. machine learning tutorial
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Комментарии
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Very clear explaination! Thank you so much!

南南東-sv
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Great Video! Thanks for making it. One minor comment is that at 6:56, sigma^2/k is actually not from CLT, essentially it's just from the basic property of variance.

yuegao
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Keep up the awesome work!, Emma I watched your video one year ago and I got a data science job. Now I start to forget some ML models that I don't use often, it is a very good way to refresh my memory on them!!!

alanzhu
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Looking forward to the notes!! Thanks for sharing, Emma!!!

evag
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Would you mind sharing the notion page with us? Would really appreciate it. :)

Doctor_monk
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Nice lecture, can we get the resource you used. It will be very helpful.

raghu_teja
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A very helpful video on RF. Hi Emma, would you mind actually making a video on how to go about mastering new ML concepts from zero to hero?

ayuumi
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Hi Emma. Thanks for the video. Have a question. I am not sure about how this statement is true. "random forest constructs a large number of trees with random bootstrap samples from the training data". If sample size = replacement, we have all observations in every bootstrap sample. Then, it's not random bootstrap samples. Can you please elaborate what that line is saying?

shawnkim
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Can you clarify how the random feature subset selection happens "without replacement"? Is it that e.g. we have 20 features and tree 1 takes 10 features, tree 2 takes the remaining10 features and now tree 3 can take 10 from the original 20?

davidskarbrevik
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What happens if RF n_estimators(individual decision trees) have conflicting outcome as in 50% of them voted/predicted class A while the other 50% voted/predicted class B.
In this situation, what would be the final outcome??

imran
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can we say if interview ask which algorithm can be used here, and we don't know the Ans we can surely apply random forest here.🤔😜

shubhamkaushik