Visual Guide to Decision Trees

[@NoDoglapan]

I am a visual learner and your visuals along with the clear explanation makes this one of the BEST tutorials I have come across.

[@francisarpoh4289]

this is the best i have watched so far. thank you for sharing

[@deepakzm]

Explanation is crystal clear and the visuals are just cherry on top!

[@Econoscent]

Thank you so much!

[@HitomiNakamura-y8n]

Better than any videos about decision trees I found online! Thank you for making this:)

[@JillianWu]

As a visual and auditory learner, this was absolutely perfect for me. Thank you!

[@Econoscent]

Wonderful!

[@elvenkim]

Absolutely luv the colorful tree graphic - an true art!

[@Econoscent]

Thank you so much 😀

[@Maria-n5g1l]

Thank you for a very clear and structured explanation! The video is very helpful

[@mehdicharife2335]

I can't explain how clear and helpful this was. Thank you!

[@Econoscent]

You're very welcome!

[@proxynpl6381]

The video is very concise and animation is also super good. This makes really great video for refreshing the ML algorithms. Thanks for your effort.

[@Econoscent]

Glad it was helpful!

[@jjulinhocesar]

You just saved my education path. Thank you so much.

[@Econoscent]

So glad to hear that!

[@adityasharma5663]

Perfect video on DT so far !! Thanks !😀

[@y_ffordd]

Thanks for this, very clear, great example

[@Econoscent]

Glad it was helpful!

[@colinbalfour1834]

At 5:00, why do you split only the false outputs of the second layer? Is this to reduce complexity like mentioned later?

[@Econoscent]

It's because the other branch is pure. So you can't really split further

[@ea1766]

nice , simple and short, Well done!

[@Econoscent]

Thank you!

[@iceiceisaac]

Wow, this is awesome! Thanks 👏

[@shorty2802]

Great intro, well explained and awesome visuals.

[@Econoscent]

Thank you! Please check out other videos in the ML 101 series and stay tuned for more! 😊

[@Ab_Jannadi]

Simple and good
Clear and beautiful.
Thanks..

[@Econoscent]

You are welcome 😊

[@ckeong9012]

awesome! May i know how do get the accuracy of 80%?

[@urielmenalled7931]

What software did you use to make these animations? They are excellent!

[@Econoscent]

After Effects. Thank you!

[@tahirhaithem9841]

very informational, learned a lot, thanks a lot.

[@Econoscent]

You're welcome!

[@HarshanVallabh]

WHAT A GREAT VIDEO
THANK YOU
BRILLIANT

[@Econoscent]

Thank you!

[@ikersanchez8222]

What's the optimal way of selecting a quartile threshold when conditioning the split of continuous variables? as in why did you choose the median as a threshold? is it something arbitrary?
Great video btw.

[@ikersanchez8222]

Mr. ChatGPT told me it's common to calculate the mean of every pair of contiguous values in each continuous variable's scale. Each mean is considered a potential threshold for splitting the data. Apply the Gini formula (or another impurity criterion) to each split, and select the one that results in the purest nodes. That's your optimal threshold.

[@amann8150]

Almost tripped sawing the clip of tree splitting 😭🛐

[@nitinyadav3094]

Thank you for such great content

[@Econoscent]

Thanks a lot! Do subscribe for more!

[@dayoutofficial1]

Great Explanation 👌👌

[@sapnasharma4476]

Very nicely explained!

[@Econoscent]

Glad it was helpful!

[@TC-xk5qh]

i love you ❤❤❤❤ thank you very much

[@Econoscent]

You're welcome 😊

[@onurrrrr77]

Great video!

[@Econoscent]

Glad you enjoyed it

[@valor36az]

Best explanation

[@Econoscent]

Glad you think so!

[@anands9112]

definition of epic

[@Econoscent]

Thanks a lot! This gives us more motivation to make videos!

[@saikun0293]

Crisp, neat and perfecto

[@Econoscent]

Thanks a lot!

[@jakubtyityis9699]

Thank you!

[@Econoscent]

You're welcome!

[@svenGPT]

Wow, thank you!

[@Econoscent]

Thanks for watching!

[@Shady9]

perfect

[@bArda26]

Nice!

[@Econoscent]

Thank you! Please check out other videos in the ML 101 series and stay tuned for more! 😊

[@amann8150]

Almost tripped sawing the clip of tree splitting 😭🛐