วีดีโอ

I faced the issue of "quasi-separation: A fraction 0.12 observation. Is that bad or good? Can you tell me more about quasi-separation and how I should fix it.

Simple & Genius ....

🔥🔥🔥

great video, well explained!

Hi Smail. Thanks a lot for sharing this content. Suppose you have 150 fetures. How to to fix the error "Maximum number of iterations has been exceeded" ?. Current function value: 0.287021 Iterations: 35

please share the github link of this code.

explain prc curve

ismail Hocam , aşağıdaki kod da sorun ne hocam? iris df <- iris[, -5] scaledDf <- scale(df) scaledDf <- as.data.frame(scaledDf) str(scaledDf) set.seed(245) Sam <- sample(1:nrow(scaledDf),size = 0.75*nrow(scaledDf)) traindata <- scaledDf[Sam, ] testdata <- scaledDf[-Sam, ] nn <- neuralnet(Sepal.Length ~ ., data = traindata, hidden = c(2,2), threshold = 0.01, stepmax = 1e+05, linear.output = TRUE) tahmin1 <- predict(nn, testdata$Sepal.Length) Error in if (ncol(newdata) == length(object$model.list$variables)) { : argument is of length zero

Thank you for the great lecture. Could you demonstrate when lead time is longer than review period?

You just saved me!! Thank you <3

Where is the part II of this series.. ?

Sir, you saved my day ❤️ I was struggling with my neural network bcz of not normalising the input and outputs... As you said, i have normalised them and then the MSE was reduced close to 5E-5. Now the prediction is almost perfect

The code used please

Thank you!!

Hi is it possible to export the summary from python to excel ?

ur pronunciation is bad

Great Explanation Thank Sir

Sağ olun hocam. Kek heuristicmiş

Thank you

What if review period is shorter than lead time, meaning review periods overlap before replenishment happens

I really don't like this trial-and-error approach where people tune the various degrees of freedom of the system to reach a non well defined goal. It gives no information about what you're trying to achieve and why, hence when things don't work you have no idea why and when things work you still have no idea why, giving the chance to unpredictable catastrophes to happen. There's an insane vastness of theory and literature available for these topics which shall be better to understand (not fully) at least the philoshopy you're following.

In the last model the p value of agr is so high then why you have used As it is so insignificant

Hi sir. How do you adjust the forecast with the bias. Pls let me know the formula.

Brilliant - can't thank you enough!

Thank You, nice explanation dan really great work. Keep it up

thank you. appreciate your time and explanation

Thanks

Easy but efficient

Thak you sir

great video sir

hello, can I get the source code?

Can u just add the dataset in discription box

can you do something similar for a ensemble model regressor/tree regressor? or does this require a different statistical test?

Thanks this saved me an hour

Good content! Thank you

Grear job. How can i extrapolate the odds value from the model?

Thanks for information. What if we need two closest neighbors? will this algorithm work?

Hello, I tried to import statesmodels.api but I got an error message saying ModulenotFound Error. how do I add this library to my jupyter notebook

Isn't the SS value incorrect? sqrt of 3 is 1.732, hence SS should be equal to 228, don't you think?

Good video, dataset but I could find the dataset

You have helped me a lot, Thank you❤️

Very good video for an absolute beginner like me! So how can I plot the graphs to show the best performance in terms of the R2 against the number of epochs or in this case the max_iter number?

Thanks

Saviour

I have just used a random dataset...but that provide me result of scaler fitting between -1 to 0...and accuracy level more than 1....what is the reason behind this?

Great video, God bless you

Can I ask for the IPYNB file

Что такое head?

Sir, would you share your dataset? Or where I can download the dataset that is the same as yours? Thank you for the explanation about covariance sir.

thank you