Model Evaluation : ROC Curve, Confusion Matrix, Accuracy Ratio | Data Science

In this video you will learn about the different performance matrix used for model evaludation such as Receiver Operating Charateristics, Confusion matrix, Accuracy. This is used very well in evauating classfication models like deicision tree, Logistic regression, SVM
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[@BigEdu]

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[@suwarnachoudhary8591]

Rare information provided in least time. Outstanding. Thank you so much.

[@BigEdu]

thanks

[@osman1156]

Great explanation and to the point (thumbs up)

[@BigEdu]

thanks

[@amruthagaddale8551]

Helped a lot. Thankyou soo much

[@christinaahn1287]

great great!!

[@anouarbelaid1741]

very good video thank you soo much

[@BigEdu]

thanks

[@dalwindersingh5902]

confusion matrix stated is wrong : ->
Here is the correct version
Happy cases
1) string1 (T)+ string2(P) -> Actual + prediction -> TP -> actual is 1 and prediction is 1
-> actual values is true and prediction is also true -> Happy case -> Box (a) in figure of video
2) string1 (F)+ string2(N) -> Actual + prediction -> FN -> actual is 0 and prediction is 0
-> actual values is False and prediction is also False-> Happy case-> Box (d) in figure of video
Sad Cases
3) string1(T) + string2(N) -> Actual + prediction -> TN -> actual is 1 and prediction is 0
-> actual values is True and prediction is false -> Box (b) in figure of video
3) string1(F) + string2(P) -> Actual + prediction -> FP -> actual is 1 and prediction is 0
-> actual values is false and prediction is true -> Box (a) in figure of video

[@dalwindersingh5902]

any good video

[@BigEdu]

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[@bhupensinha3767]

Where was ROC explained?

[@naveedrahman4556]

In the video

[@arifnaim1]

Thank you so much !!!!

[@BigEdu]

thanks

[@mka11221]

did you really farted at 10:46