Confusion Matrix in Classification with Simple Example| Machine Learning in Tamil

[@manokar484]

Superr mam nice work🎉

[@Dr.NancyJane]

Thank you 😊

[@mahalakshmi9280]

Thank you, madam .clearly explained the confusion matrix concept.

[@Dr.NancyJane]

You're most welcome

[@ManokarSelvaraj]

Sensitivity=87.5℅

[@AJITHKUMAR-hn4fm]

0.875. Mam explain also about the 3X3 matrix

[@Dr.NancyJane]

👏.. sure..

[@ravimsr8872]

You have mentioned the definition of Precision as "How many correct predictions out of all Predictions?. This I feel is the definition of Accuracy. Can you please let me know the verbal definition of Accuracy and Precision as a response to my doubt? Thanks.

[@Dr.NancyJane]

Accuracy measures the proportion of correct predictions out of total predictions made. It's calculated as:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
Precision measures the proportion of true positives among all positive predictions made. It's calculated as:
Precision = TP / (TP + FP)

[@Zmarteditz]

Mam don’t stop uploading videos plss update more videos

[@dhanushyt1312]

FN and FP position is interchanged

[@Dr.NancyJane]

Hi the position of FN and FP in the confusion matrix depends on how we represent the actual and predicted output (i.e) row wise or column wise. Here actual output is taken in row wise and predicted output column wise..

[@dhanushyt1312]

@@Dr.NancyJane ok thank you mam