Confusion Matrix Solved Example Accuracy Precision Recall F1 Score Prevalence by Mahesh Huddar

Confusion Matrix Solved Example Accuracy, Precision, Recall, F1 Score, Sensitivity, Specificity Prevalence in Machine Learning by Mahesh Huddar
The following concepts are discussed:
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confusion matrix in ML,
confusion matrix terminology,
performance of a classification model,
Performance metric,
performance metric machine learning,
example confusion matrix for the binary classifier,
Accuracy machine learning model,
Misclassification Rate in ML,
True Positive Rate of classifier
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[@MaheshHuddar]

F1 score = (2*precision*recall)/(precision+recall)

[@cromllo7162]

how did you get the answers in percentages?

[@notfixedcontent_12]

Multiply with 100

[@mjay5655]

i got 95 as the answer for F1

[@tuheen7668]

Now I know why it is called CONFUSION matrix......but you explained very well sir!

[@MaheshHuddar]

Thank You
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[@user-vl6ks6yi9j]

Best Statistical explanation I have ever encountered thanks 😊

[@MaheshHuddar]

Welcome
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[@spoorthib2496]

If more than one model is present based on what we need to select one particular model ie based on high accuracy or precision or recall?

[@menagasaravanan1027]

Thanks u sir ur explanation is superb

[@MaheshHuddar]

Welcome
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[@Rider-jn6zh]

Can u please let us know when and which accuracy metric we can consider for our problem among accuracy, precision,recall and f1-score

[@abizarkhalil]

plz calculate WEIGHTED ACCURACY from the confusion matrix (two class)

[@ShivamSingh-te9tl]

Thank you so much sir, and also Happy Teacher's day :)

[@MaheshHuddar]

Welcome
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[@insha9862]

Thank you very much sir

[@AR_abbasi]

Hii

[@MaheshHuddar]

Most welcome
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[@Xmenbasketbol]

Thanks for the simple explanation

[@MaheshHuddar]

Welcome
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[@thestudyexplanation7052]

THNAKSS UUUU SOO MUCHHHH SIRRRRR :))))

[@MaheshHuddar]

Welcome
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[@3srkingdom]

Thanku sir❤

[@MaheshHuddar]

Welcome
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[@AJAmr99]

thank you, bro

[@MaheshHuddar]

Welcome
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[@sakshikadel16]

thank you sir understood very well....

[@MaheshHuddar]

You are most welcome
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[@sakshikadel16]

@@MaheshHuddar sure sir

[@jishnupm7144]

thanks

[@MaheshHuddar]

Welcome
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[@VineethMenonk]

Super bhai

[@MaheshHuddar]

Thank You
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[@chrisogonas]

Superb!

[@MaheshHuddar]

Thank You
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[@chrisogonas]

@@MaheshHuddar Absolutely!

[@Zaid-bf1qp]

How about support?

[@a_13_ashishgaikwad]

Thanks Sir

[@MaheshHuddar]

Welcome
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[@RD-td6hg]

Thank you. This shit confuses the hell out of me. You explained it very well

[@MaheshHuddar]

Welcome
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[@PSTWFB]

#confusion#matrix#machinelearning#deep#precision#recall F1 #score#accuracy#true#positive #negative!
th-cam.com/video/YlFgsaxagX0/w-d-xo.html

[@user-cf7uz9bq4q]

nicer sir thanks

[@MaheshHuddar]

Welcome
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[@mohamedjamaludeen7179]

Nice

[@MaheshHuddar]

Welcome
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[@santoshkumarsahu8482]

When we prefer accuracy, precision, recall and f1 score??

[@notfixedcontent_12]

Thank You sir
Very much

[@MaheshHuddar]

Welcome
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[@rithikareddy3821]

What is no information rate?

[@ahsanmemon8943]

What can we do for a big matrix 10*10

[@alphapranay214]

Pooja 🫖

[@humbleakh]

​@@alphapranay214😂😂

[@black8august410]

​@@alphapranay214 😆

[@watchdesk8191]

but the accuracy when calculated is , 45.63333 .... how does it come 93.33 % to uh sir ?

[@MaheshHuddar]

You are wrong
Watch video one more time carefully

[@areejawan]

@@MaheshHuddar ans is 140/150 equals to 0.9333

[@notfixedcontent_12]

To get the value in presentage we need multiple with 100

[@kentimancy]

legend

[@MaheshHuddar]

Welcome
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[@maanwit4318]

Send me the example please

[@asifnazir8965]

f1 score?

[@MaheshHuddar]

F1 score = (2*precision*recall)/(precision+recall)

[@asifnazir8965]

Thank you so much sir..u r the best teacher...live long🥰🥰🥰

[@MaheshHuddar]

@@asifnazir8965 Thank You
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[@amitgupta624]

Thank you sir

[@MaheshHuddar]

Welcome
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[@Elena_9152]

Thank you sir

[@MaheshHuddar]

Welcome
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