Such an underrated channel for how accurate and straight to point is the content compared to other channels with lots of unnecessary talks....you earned a subscriber!, I'm definitely recommending your channel if anyone enquired about your content
Very well explained I was struggling with many things and your accurate explaination cleared many doubts. Thankyou and please countinue uploading. This was Veeeery helpful !
As I am teaching myself about Logistic Regression with SKLearn, I read in the documentation that fit_intercept defaults to True, so it would have been running in all your calculations.I'm wondering why it was added as True?
I have a question. 1) .score that represent R-square in Logistic Regression model. Why it can interpret accuracy of the regression? 2) I use confusion matrix to find accuracy. is it a same solution or not and why?
In classification you can get exact same class. So accuracy can be a metric and you can canculate confusion matrix. But in regression exact match doesn't happen usually. If you try to find accuracy you will get 0% accuracy most of the time. The prediction is mostly an estimation. So you evaluate the model by using R-squared or mean absolute error or mean squared error, or something similar.
aren't we supposed to use "family = sm.families.Binomial()" when building the logistic regression model in "sm.GLM(y_train, X_train, family = sm.families.Binomial())" Please explain.
Just clear and simple, easy to understand. Many thanks.
Such an underrated channel for how accurate and straight to point is the content compared to other channels with lots of unnecessary talks....you earned a subscriber!, I'm definitely recommending your channel if anyone enquired about your content
Honestly!!!!🤧🤧
Big kudos to your teachings
Thank you so much for this amazing explanation. You're the best at explaining logistic regression! You deserve way more recognition!
Thanks for your sharing, I currently get trapped in how to conduct logistic regression model in Python, you just save me, thanks a lot again!
Thank you SO MUCH for this. You're an amazing teacher, and this class was definitely really helpful!
Took me about an hour but this was my first introduction to Python. thanks!
Very well explained I was struggling with many things and your accurate explaination cleared many doubts. Thankyou and please countinue uploading. This was Veeeery helpful !
awesome implementation of logistic regression. Another way to measure the accuracy is to use confusion matrix and accuracy score metrics
Thank you so much for your time and effort !!! I am waiting for your next videos about other argorithms for the classification problems !!
Well Explained the complicated Algorithms....Thank You.
Thank you so much for this!!!!! You have no idea how much it helps :D
Thanks a lot for your explanation, it gives me a new sight for this topic
Request for more Machine Learning Videos in details.
at last something hands on with real problems. I'm tired of maths abstract bullshit formulas....
Hi your voice sooooo calm and cool
Upload more video
by using log_reg.score() for each (x,train ,and x_ test) it replace the other sq errors right?
Thank you! this is excellent
As I am teaching myself about Logistic Regression with SKLearn, I read in the documentation that fit_intercept defaults to True, so it would have been running in all your calculations.I'm wondering why it was added as True?
can you make a video and explain how to visualize logistic regression?
Your video is so much helpful... But there is voice problem..sometimes its became high
Yes, I noticed it. I just couldn't find time to redo it. So uploaded it as it is. Thanks for watching! and Sorry about that experience!
I just love you so much right now
So how to know which of the independent variables strongly impacts the dependent variable?
Do correlation with variables through Berson(r)
all clear, to the point video with 0 nonsense. thanks a lot
when we use cat.cosd and one hot encoder ?
why u didnt use one hot encoder ? please im confused with this
I have a question.
1) .score that represent R-square in Logistic Regression model. Why it can interpret accuracy of the regression?
2) I use confusion matrix to find accuracy. is it a same solution or not and why?
In classification you can get exact same class. So accuracy can be a metric and you can canculate confusion matrix. But in regression exact match doesn't happen usually. If you try to find accuracy you will get 0% accuracy most of the time. The prediction is mostly an estimation. So you evaluate the model by using R-squared or mean absolute error or mean squared error, or something similar.
@@regenerativetoday4244 Thank you so much!!!! I appreciate it
aren't we supposed to use "family = sm.families.Binomial()" when building the logistic regression model in "sm.GLM(y_train, X_train, family = sm.families.Binomial())"
Please explain.
Perfect
Thanks a lot !!!
simple and very easy to understand ....loved it!!!
do you have an instagram channel?
🤩🤩
Thanks a lot
Can you explain why random state=0
It is done so the ressults are reproducible. The numbers generated will be the same on your computer if you set that state
Thaaaaaaaaanks