TP, FP, TN, FN, Accuracy, Precision, Recall, F1-Score, Sensitivity, Specificity, ROC, AUC

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In this video, we cover the definitions that revolve around classification evaluation - True Positive, False Positive, True Negative, False Negative, Accuracy, Precision, Recall, F1-Score, Sensitivity, Specificity, ROC, AUC

These metrics are widely used in machine learning, data science, and statistical analysis.

#machinelearning #datascience #statistics #explanation #explained

VIDEO CHAPTERS
0:00 Introduction
1:15 True Positive, False Positive, True Negative, False Negative
6:08 Accuracy, Precision, Recall, F1-Score
8:59 Sensitivity, Specificity
10:30 ROC, AUC

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Комментарии

Thanks for taking Time and explaining so well

guetsenelson

Thank you. This is much understandable than my textbook

namelessbecky

Great video! Suggestion: Normalize volume to 50% going forward as I really had to crank up the speakers to hear your voice.

houstonfirefox

Good Content
Subscribed right away!!!

mrsmurf

Very well explained. Thank you very much. I just pressed the Subscribe button :)

thewisearchitect

its great simple video will be great to do more videos showing the over fitting and under fitting and other questions that normally been on interviews

dhualshammaa

Thank you for the vid 👍
But what do you mean by "thresholds" at 11:10 ?
Like, what are the thresholds in terms of neural networks, and how can we change them?
Thank you :)

my_master

Can I have these slides please within respective concern 🙏💓

muhammadanasali

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