Excellent

Your explanations are obvious, they remind me of all the knowledge that I had forgotten after my statistical class in the second year. I'm doing my capstone project, and your videos help me a lot. I appreciate your attribution.

Thank you bro, i really needed this video because i was struggling a little bit with probabilities ✨🙏

Man using manhattan distance really amps up the speed to reach desired result

great video man

Please make a video on isolation forest

Loved how u said in the intro "people from the future". Kind of a new intro for me that i have experienced today.

You're an excellent teacher👍

Awesome video! You explained the concept so much better than many university lectures. I would just like to make a quick point about the random feature selection step when bootstrapping. (imho) According to most recent/popular papers on random forest algorithms, the most common and efficient approach appears to randomly select feature subsets at each node when traversing each tree rather than selecting it only once at the tree level. One explanation I found online is that "while sampling features at every node still allows the trees to see most variables (in different orders) and learn complex interactions, using a subsample for every tree greatly limits the amount of information that a single tree can learn. This means that trees grown in this fashion are going to be less deep, and with much higher bias, in particular for complex datasets. On the other hand, it is true that trees built this way will tend to be less correlated to each other, as they are often built on completely different subsets of features, but in most scenarios, this will not outweigh the increase in bias, therefore, giving a worse performance on most use cases." I really hope this was clear. Any comments are very welcome!

Thanks!

Asking out of Context ! Are you Bengali ?

why the x0 uses vertical line on visualization and the x1 uses horizontal line? how to describe visualization the x-axis if I have x1,x2,x3,x4?

In a certain way, this video was less about Markov chains themselves and more about the underlying directed graphs. Using different language to describe the same things, the communicating classes are called “strongly connected components”, and you can form a “condensation graph” (which is a directed acyclic graph) by collapsing these communicating states.

Good Old Maths... Man i miss it..😍

the demonstration of n gets bigger only shows cases where n is an odd number? how about even numbers like L4, L6?

video is awesome!

it was wonderful.

sooo good

Is there a video on No U-Turn Sampler (NUTS)? Thanks

The best explanations and visualizations I've ever seen. Thank you for that!

This is excellent info well presented. Thank Yoyu

Good video but there seems to be a small error. In particular, you say we are assuming X1 and X2 are independent, but we do not actually make that assumption for Naive Bayes, we assume only conditional independence (conditioned on the class label) which does not imply general independence.

cam you make a more complicated chain?

Raise your camera so that you’re not looking down on us

When bootstrapping, how do you decide how many trees to make?

Thank you - that was a really clear explanation! 8 minutes and you made me understand the basics of Random Forest. Crazy.

terrible English

Got to about 7 minutes 30 seconds in before I needed to learn some of your fancy symbols

So clear. Thanks for explaining. Also the background music was super calming

thx

Thank you so much

Man you're really good at explaining things simply and visually love your work

How did we create the 4 random trees? Like how do we choose root node and the leaf nodes. What was the criteria?

thank you

Very good and clear explanation ,thank you :D

Great!

It's such a great explanation! Thank you so much!

Excellent explanation. Thumbs up and subscribed! Thank you!

great video! I still got a question tho: how did you apply the variance formula to get those starting variance values before applying the vairance reduction formula? i do not understand how the number 9744 came up

Wonderful!

Very good explanation, thank you. On a side note, I wish we could use more descriptive notation, like P(R) for the probability of rain. It would make things much clearer.

yeah, wow! Really well done!

Why red and green? 10% of thenpopulation does not see the difference between the small dots... next time maybe circle and squeres.

The colours green and blue are appearing similar on the image. You may use some other method or colour (like red, green, yellow).

Thank you. It was great.

Could not understand variance reduction part. If we're talking about the variance reduction, then as per your explanation, 2nd set should have been chosen, but you selected the first set. Am I assuming something wrong here?

i didnt understand the statement at 7:52

looks like you're the blueberry

Thanks, got saved for Deep Learning exam

I NEVER eat pizza the day after i ate hotdog!