Another amazing video , your content is just top notch . best channel for data science in making.

WOW what a video, I have read books and watched videos, but after wathcing this twice i can say i understood this to its fullest Your Intuition is perfect. 👍

The minute I found you on Twitter, I knew you were great at breaking down concepts in ML. This video just proves it more. I can say I've learnt a lot from oxbnomial's daily questions, your consistent content and your youtube videos. Thanks a lot. You have a fan in me. Thank you for this and all you do.

Is it true that You need to know classic Computer Vision to learn modern Computer Vision? What is roadmap for computer vision?

all is good, except for the hitting desk part, which is frightening .

3 minutes in and I'm glad that you made this video. Thank you 🙌

Sir Your explanation is awesome No words to define

I think there is a mistake in 5:34minutes because False Positive A false positive occurs when a model predicts a positive outcome (e.g., an event will happen, a person has a disease) but the actual outcome is negative. So 7 is FP In simpler terms, it's a false alarm. False Negative A false negative occurs when a model predicts a negative outcome (e.g., an event won't happen, a person doesn't have a disease) but the actual outcome is positive. So 13 is FN This is often considered more serious than a false positive in many cases, as it misses a critical event or condition.

very nice video. Better to focus camera on chart for more time

Thank you for your energy 💪💪

very clear and simple explanation thanks you very much

Can you make a video on how to use binary classifiers for multi-class classification?

thanks brother!!!😄😄😄

There's a similar pattern in your video and bnomial question 🧐

Absolutely crazy video, thank you.

Amazing!

Thank you for the explanation :)

perfectly explained

Thanks for the video but when I try to solve this example (explain below), things totally went wrong, I will be very pleased if you made a short video on it in the similar fashion. Thanks in advance. Example: There are two possible predicted classes: "yes" and "no". If we were predicting the presence of a disease, for example, "yes" would mean they have the disease, and "no" would mean they don't have the disease. The classifier made a total of 165 predictions (e.g., 165 patients were being tested for the presence of that disease). Out of those 165 cases, the classifier predicted "yes" 110 times, and "no" 55 times. In reality, 105 patients in the sample have the disease, and 60 patients do not.

I'm not good at English, but this video helps me understand what is confusion matrix more than my mother language video did lmao :")

Another nice video!

Such a great video Thank you so much !!!! This helps me a lot

Loved this video

thanks for the great video!

I liked one of your last videos but in this one, I think the Wikipedia page (explaining the Confusion Matrix) beats you in clarity, and it also clarifies some other relevant metrics (not only accuracy).

Your audio is clipping I think.

class A could be the negative class too and class B as positivwee

should've paid you my uni fees instead

Amazing!