Is that a Utah State shirt!?? I'm a Ute but always cheer for the Aggies! Your videos are extremely helpful! Thank you for taking the time to make these videos!
Thanks for the video, but it seems like this doesn't work when the sizes of training and testing data are not equal. It gives a warning message for predict function and then gives probabilities in the size of training data. Could you explain why this happens?
Hi Abbas this is a great video. Are you planning to make a video on goodness of fit for logistic model? I understand you are using misclassification rates using .5 as cut off to get predicted values but that might not be applicable in many areas.
Excellent explanation, but I have a question concerning the last conclusion. According to the high misclassification rate , you said that the variables are not good. what we should do in this case? Many thanks.
This is a great walkthrough! Thanks so much for posting this. I have a question on the creation of test vs training data. Wouldn't it be less biased to randomly select rows? For example use like runif and then sort them? Just curious as I'm studying this now
How does the (for) loop looks like if i want to do the logistic regression procedure on 100 different trainingset (so provides me 100 different models) and make predictions based on this 100 models upon one and the same test set?
can you create a training video on conditional logistic regression (matched analysis) when there are 1:1 matched case controls, but 100,000 of observations?
Can you upload the datasets for your videos? I found one for the first video but the ones on that link don't match the later videos in particular this one, regression, and decision tree analysis
Sir, I have one problem with this lecture. When you are converting the data obtained into 0 and 1 after finding out the probabilities,u are considering that when the probability is greater than 0.5 then you are saying that the stock will go up (which is not correct - I think so). Can u please explain?
I like your tutorial. Its one of best in logistic regression examples. 1. When i try to use all independent variables eg: I got following warning. Do you know why? stock_model = glm(Direction~., data=training_data, family = "binomial") Warning messages: 1: glm.fit: algorithm did not converge 2: glm.fit: fitted probabilities numerically 0 or 1 occurred 2. Also, how to identify important variable in logistics regression? Thx in advance.
Excellent explanation, this is EXACTLY what I needed to help me understand this operation in R. Thanks for taking the time to make this!
Thanks, hundreds of web search finally became helpful.
very very good explanation..appreciate your effort!! keep continuing the good work!
Thanks for good walk thru to logistics regression.
Your concluding remarks were awesome "flipping coins might give you better results" :)
Very well explained! :D I request you to teach different types of regression - Lasso, Ridge, Quantile, Bayesian, Jackknife, etc.
Is that a Utah State shirt!?? I'm a Ute but always cheer for the Aggies! Your videos are extremely helpful! Thank you for taking the time to make these videos!
Thanks for sharing, this has been MOST helpful
Thanks for the video, but it seems like this doesn't work when the sizes of training and testing data are not equal. It gives a warning message for predict function and then gives probabilities in the size of training data. Could you explain why this happens?
Hi Abbas this is a great video. Are you planning to make a video on goodness of fit for logistic model? I understand you are using misclassification rates using .5 as cut off to get predicted values but that might not be applicable in many areas.
Excellent explanation, but I have a question concerning the last conclusion. According to the high misclassification rate , you said that the variables are not good. what we should do in this case?
Many thanks.
This is a great walkthrough! Thanks so much for posting this. I have a question on the creation of test vs training data. Wouldn't it be less biased to randomly select rows? For example use like runif and then sort them? Just curious as I'm studying this now
How does the (for) loop looks like if i want to do the logistic regression procedure on 100 different trainingset (so provides me 100 different models) and make predictions based on this 100 models upon one and the same test set?
Could you share about the lift chart in R for logistic regression ? I come from a SAS background.
can you create a training video on conditional logistic regression (matched analysis) when there are 1:1 matched case controls, but 100,000 of observations?
Not able to subset Direction by 'test' data set - invalid subscript of type 'list'
I will subset as Direction[999:1250]
superb keep posting videos on r bro please
Can you upload the datasets for your videos? I found one for the first video but the ones on that link don't match the later videos in particular this one, regression, and decision tree analysis
They can be found in the ISLR package, see here: cran.r-project.org/web/packages/ISLR/index.html
Sir, I have one problem with this lecture. When you are converting the data obtained into 0 and 1 after finding out the probabilities,u are considering that when the probability is greater than 0.5 then you are saying that the stock will go up (which is not correct - I think so). Can u please explain?
I like your tutorial. Its one of best in logistic regression examples.
1. When i try to use all independent variables eg: I got following warning. Do you know why?
stock_model = glm(Direction~., data=training_data, family = "binomial")
Warning messages:
1: glm.fit: algorithm did not converge
2: glm.fit: fitted probabilities numerically 0 or 1 occurred
2. Also, how to identify important variable in logistics regression?
Thx in advance.
It may be because, In the dataset, one particular variable for all observations may be same or zero.
a) This was very helpful. Thanks!
b) LOL at the actual model you built. This is not helping me meet my investment goals.
Hahahha, thanks Dan. flipping a coin might give you better results. LOL
was of great help, i tried doing this on indian index i.e cnx nifty and got misclassification error of just 14 %
What is the accuracy value?
very good!
tank you
Hi i have done same coding as u but missclasifi. error is 69 % how ? because yours is 51 % ? why it is diff. ? done all same as u.
Misclassification error varies with the data set.
Warning message:
glm.fit: fitted probabilities numerically 0 or 1 occurred