The Confusion Matrix in Machine Learning

One of the simplest and most popular tools to analyze the performance of a classification model.
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[@soleymannasiri3134]

Thank you for your energy 💪💪

[@ayoyinkasofuwa7916]

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.

[@PSTWFB]

#confusion#matrix#machinelearning#deep#precision#recall F1 #score#accuracy#true#positive #negative! kzbin.info/www/bejne/j52pmKaXrcaajpI

[@Balkarsingh-qt5ky]

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

[@underfitted]

Wow, thank you!

[@1lpelato]

Thank you for the explanation :)

[@saikrishnasirikonda1008]

Absolutely crazy video, thank you.

[@atharvavetal453]

Loved this video

[@mmk8072]

Amazing!

[@thublabla]

thanks for the great video!

[@mgreek31]

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. 👍

[@underfitted]

I’m glad it helped!

[@rissyq6662]

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

[@underfitted]

Thanks!

[@satishkumar19871]

very clear and simple explanation thanks you very much

[@underfitted]

Thanks for commenting! Glad it was helpful!

[@Offiziersmesser]

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

[@underfitted]

Glad you enjoyed it!

[@Offiziersmesser]

@@underfitted I binge-watched most of your videos. Can't wait for more.

[@pukaka6]

perfectly explained

[@Param3021]

Another nice video!

[@underfitted]

Thank you so much!

[@saadvadanagara3276]

Sir Your explanation is awesome No words to define

[@underfitted]

Thanks a ton!

[@padmageethabg3409]

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

[@hazelding]

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

[@Darkev77]

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

[@underfitted]

This is a great idea! I will add it to my list.

[@convolutionalnn2582]

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

[@thublabla]

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

[@faizanramzan1393]

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.

[@Param3021]

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

[@underfitted]

I don't doubt it :)

[@morphos2]

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).

[@underfitted]

Yup, that’s the way it goes :) I can’t win them all.

[@enonu]

Your audio is clipping I think.

[@underfitted]

Not good. I’ll need to keep an eye on that.

[@ashwinshetgaonkar6329]

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

[@underfitted]

Right on!

[@kozaTG]

should've paid you my uni fees instead

[@thevoyager7675]

Amazing!