Accuracy and Confusion Matrix | Type 1 and Type 2 Errors | Classification Metrics Part 1

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In this video. we'll explore accuracy and the confusion matrix, unraveling the concepts of Type 1 and Type 2 errors. Join us on this journey to understand how these metrics play a crucial role in evaluating the performance of classification models.

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⌚Time Stamps⌚

00:00 - Intro
00:46 - Accuracy
06:25 - Code Example using SKL
08:35 - Accuracy of multi-classification problem
10:18 - How much accuracy is good?
13:16 - The problem with Accuracy Score
15:30 - Confusion Matrix
23:15 - Type 1 Error
25:53 - Confusion Matrix for Multi Classification Problem
30:03 - When is accuracy misleading?

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Nitish Sir is Bhagwaan of Machine Learning realm.

No one comes near you sir . Top class teaching skills.

jinks

a situation where accuracy score can mislead us is when the cost of false positives and false negatives are different. For example, in medical diagnosis, a false negative (saying a patient is healthy when they are actually sick) may be much more costly than a false positive (saying a patient is sick when they are actually healthy). In this case, a model that minimizes false negatives may be more desirable, even if it has a lower overall accuracy.

talibdaryabi

no words ♥Sir please complete all 100 days

thatsfantastic

Thanks for the amazing explaination, just a small correction in sklearn the order of tp, tn, fp, fn is different for binary classification then what you quoted.

in sk learn it is:
"By definition a confusion matrix C is such that Ci, j is equal to the number of observations known to be in group i and predicted
to be in group j.
Thus in binary classification, the count of true negatives is C0, 0, false negatives is C1, 0, true positives is C1, 1 and false positives is
C0, 1."

trayambrathore

Thanks for this wonderful playlist sir. Your content is always on point and easy to understand. I just have a doubt about multivariable classification models. Like you showed at 27:23 where we our model has predicted 0 items for Type 1 but it is for Type 0. I couldn't get that part. Can someone please explain?

akshaysingh

The way you teach is literally awesome. Thank you so much for this lecture.

WinYourSoul

Always wondered by ur thorough knowledge & way of explaining it. U explains everything very deeply & in a anyone will understand. Thanku so much. No. 1 Campus X.

siyays

is there a way to know for which 6 people the model did mistake

nishantdey

Well explained!! Thanks much for this tutorial!

harikrishna-harrypth

what if I choose (Heart disease = 0) and ( Not a Heart Disease= 1) ?
In this situation my confusion matrix
answer will be different from you.
so when should we choose 1 or 0 value ?

Shashank_Shahi

Great Explanation !@ Thankyou so much Sir..

Cric_best_ininings

Kindly correct the confusion matrix 19:10 it looks like you have misplaced the FP & FN.

devenderdaila

I think confusion matrix is not correct, actual along the col, predicted along the row
from sklearn.metrics import confusion_matrix
y_true = [1, 0, 1, 0, 0, 1]
y_pred = [0, 0, 1, 1, 0, 1]
confusion_matrix(y_true, y_pred)
array([[2, 1],
[1, 2]], dtype=int64)
y_pred = [0, 0, 1, 1, 0, 0]
confusion_matrix(y_true, y_pred)
array([[2, 1],
[2, 1]], dtype=int64)

Look at this example...

Let me know if i am wrong...

YogaNarasimhaEpuri

sir apne t-shirt bhi vahi pehnee hey " Whats the score"

tanmayshinde

now i understand why name of confusion matrix start with confusion😆😆

AyeshaAyesha-ii

Why LR is called regression, even though it is classification algo?

vikashkumar-cree

Confusion matrix jaisa name waisa kaam🥲

flakky

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