This is daily routine for me to visit your channel and learn important concepts with great understanding. Thank You Nitish sir ❤❤🙌. CampusX - Best Place for all the ML/DL Enthusiast .
bias : the inability of the model to truly capture the relationship in the training data variance : it measures how much the predictions of a model vary for different training datasets. overfitting : training error low and test error high. underfitting : training error high
Awesome. I like your videos because you explain the crux of the concept. However, it would be even better if you could include the formal definition of the concepts on-screen. Nevertheless, your videos are 10 on 10 bro.
Hello sir, I have a doubt about this topic. If my test R2 is increasing and train r2 is decreasing does it mean that my model has a problem with underfitting and having high bias?
This is daily routine for me to visit your channel and learn important concepts with great understanding.
Thank You Nitish sir ❤❤🙌.
CampusX - Best Place for all the ML/DL Enthusiast .
By Far The Most Awesome Lecture On Variance Trade-off...I'm Literally Stunned !!! 😍🤩
The best explaination for Bias-Variance Trade off on UA-cam!! Thanks a lot
Incredibly delivered the concept in very clear manner.
Loved it sir!!!
Great keep it up sir
Thanks for make Under-over fitting concepts make easier.
What a explanation. When in interview I answered this I was right though but now I am feeling more confident , will never forget this.
Best explaination I have found so far.. Thank u..
You are a great ML guru! I highly apprecitate your time and effort towards the betterment of ML world!
this playlist is by far superier than any ML paid course available online
Then you should definitely check out my Data Science Mentorship Program ;)
Great yrr
Best teacher
Thanks Sir ji, Very helpful lecture.😀
Amazing video 💞
you are truly a gem for us...cant thank enough ...keep going
8 minutes of gold
bias : the inability of the model to truly capture the relationship in the training data
variance : it measures how much the predictions of a model vary for different training datasets.
overfitting : training error low and test error high.
underfitting : training error high
Clearly Explained!
last few hrs i was stuglling with this topic now i got it
Awesome. I like your videos because you explain the crux of the concept. However, it would be even better if you could include the formal definition of the concepts on-screen. Nevertheless, your videos are 10 on 10 bro.
Feed back taken
Thank you ♥️
Really nice you have explained...Thank you!!...Can you also make conceptual vedios on mathematical modelling
Thank you sir
great
awesome
Super sir
Hello sir, I have a doubt about this topic. If my test R2 is increasing and train r2 is decreasing does it mean that my model has a problem with underfitting and having high bias?
Is all things well @ ur end? b'se we didn't see you today. see you soon.
Could you share the syllabus
if underfitting performs bad in both training and testing data then how come the variance is low ?
Yeah it come High variance i to have same doubt
Please use either English or Hindi as it is impossible to track what you are saying.