Read Aloud the Text Content

This audio was created by Woord's Text to Speech service by content creators from all around the world.

Text Content or SSML code:

<speak> Let's proceed further.<break strength="x-strong"/> Let's create a confusion matrix.<break strength="x-strong"/> So this is the confusion matrix. <break strength="x-strong"/> So here, this is a True positive, and this is a false positive. <break strength="x-strong"/> Here, this is a True negative, and this is a false negative.<break strength="x-strong"/>  So in total, we had 89 as one.  <break strength="x-strong"/> But the model predicted 54 correctly and mis classified 35. <break strength="x-strong"/> Here we had 167 as 0.<break strength="x-strong"/> And the model predicted 142 correctly and mis classified 25.<break strength="x-strong"/> Let's run this classification report. <break strength="x-strong"/> Here we can see that these are correlated.<break strength="x-strong"/> So I will explain to you, here we can see 89, which is 54+35.<break strength="x-strong"/> And here, we have 167, which is 142+25. <break strength="x-strong"/> So this is 89, and this is 167. <break strength="x-strong"/> So here, these are True positive, and these are false positive.<break strength="x-strong"/> Here, this is a True negative, and this is a false negative.<break strength="x-strong"/> Here, the total was 167 values where the label was 0, but the model predicted is only 142, and it misclassified 25. <break strength="x-strong"/> Here the total 89 values were one. <break strength="x-strong"/>But the model classified is only 54, and it misclassified 35. <break strength="x-strong"/> So here, 0 means a patient doesn't have diabetes.<break strength="x-strong"/> And one means the patient has diabetes. <break strength="x-strong"/> So here we can see that we have precession for 0 is 80.<break strength="x-strong"/> And its recall is 0.85.<break strength="x-strong"/> And we have f 1 score of 0.83.<break strength="x-strong"/> And for label 1, we have a precision of 0.68.<break strength="x-strong"/> And we have a recall of 0.61.<break strength="x-strong"/> And we have an f1 score of 0.64.<break strength="x-strong"/> Here, we have 167 total labels as 0 in the test set because we are predicting this on the test set.<break strength="x-strong"/> And we have 89 labels as 1 in this test set.<break strength="x-strong"/> And we got 0.77 as accuracy for these 256 labels. <break strength="x-strong"/> </speak>