The Confusion Matrix : Data Science Basics

[@harshsharma5768]

Confusion Matrix is indeed confusing at first, but after taking a deep glance at it and figuring out what those FP, FN words really convey, it's really easy!

[@danielkarasek1198]

Great video!! BTW I hate the TruePositive, FalsePositive etc, taxonomy because It's so CONFUSING (heh), I prefer to use False alarm(FalsePositive), and Missed (or Missed defect, or something in this way - FalseNegative), because I can much easier imagine the Red alarm going insane in some building "sir rockets are coming" and then someone just says "oooh its just false alarm". And I can easily imagine another similiar situation for the Missed example..

[@ritvikmath]

Totally agree. After several years in data science I still need to spend an embarrassing amount of time understanding what a false positive means

[@THerath361]

As always, it is another great video. I would like to hear from you about the multi-class classification model assessments.

[@parwizibrahimi5150]

Thanks for the awesome content. I could not get quit well the Recall. Please explain a bit more here. Thanks

[@PSTWFB]

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

[@albertoallenderamirez1706]

Thanks for the explanation.

[@ritvikmath]

You're welcome!

[@michaelmoore7568]

It seems to me that a confusion matrix is very similar to a Chi-Square result? My prof had us summarize Chi-Square with table as well.