Logistic Regression with Imbalanced data: A Geometric View

SVM will not have the same problem as it is only dependent on support vectors right?

great explanation sir.

Very informative video

How to measure the performance of the model for such imbalanced data sets??

This is, BY FAR, the best explanation on how imbalanced datasets damage data modeling results. It can be extended to other algorithms, such as SVM. Cheers from Brazil and thanks a tons, sir!

How the equation came yWx could you please explain and how that 0.8 is assumed could you please tell those tiny details

Haven't found this kind of explanation anywhere 🙂

Sir can u post eda data loading vedio

👌👌👌

Thanks for the explanation and we have that kinda assignment for all the linear models to understand well how the imbalance impacts the models and how the hyperparameter helps. Thankyou Team for the brilliant stuffs you have. ✌🏼✌🏼

Perfect explanation as always!!

Why not upload this with the coursework.Would be helpful to us

but if we use regularization then pi(1) will be selected right?

Fantastic explanation sir.

Tons of thanks for this brilliant explanation Sir !!!

Perfect Explanation sir.. Hats off...

Perfect explanation

So sir why to applied sigmoid function in imbalanced data set..... Use tanh function so that missclassfied values will get a value of -1 rather than 0...so in case of imbalanced data set also it will work fine..... Correct me if I am wrong?