You have the skill to simplify a complex topic which can be understood by everyone. Please continue your great work. This world needs more teachers like you.
Great video for people like me who are beginners and don't want to go deep in the Statistics part of it but a simple explanation for data science. 🧡 from India.
I loved this video. I've heard about "reducing the coefficient values" in so many other places, but you explained the 'why' behind this better than any of the others that I saw.
Really a pristine work, in explaining the ideas behind the concept. I found it really useful for having an overview look before dealing with all the math behind. Thanks
You made it easy to understand. But where do you get the alpha and slope? From the testing data set? Then the testing data set becomes the training data set.
Really appreciate the tutorial, just one query, Does regularisation always reduce the slope? I mean i think it's possible for the test dataset to have more slope than training set.
Regularisation minimises the sum of squared errors while also minimising the sum of squared magnitudes of the coefficients. This pushes the ridge coefficients closer to zero. But yes, if the penalty term is too small, the slope may resemble that of OLS.
Thank you for your short video. But I did not understand why we should minimize the slope. It is just a possibility and depends on test data. You may increase the slope to get minimum residuals.
It just feels like a fancy way to include your testing set into your training set, essentially making 100% of your data a trainingset. What is the difference between those?
The explaination is good, but I think that your example could be better. Having 3 points in the training set and 5 points in testing set is not a good practise. Also your 3 training points will give the same line every time, so again: not the best example
The example is perfect, it is for illustration, and textbooks use the same amount for training data points, it’s better to emphasize the idea of more testing data points to show the mainstream and pattern of the data, in reality, the dataset you use will never be as much as the samples it was testing or seen on. The 3 similar training data points are the same reason why the problem occurs, and the ideal mechanism for solving it is to deviate your model from it.
You have the skill to simplify a complex topic which can be understood by everyone. Please continue your great work. This world needs more teachers like you.
I've watched dozens of videos on regularization and your explanation is perfect! thanks!
Wow! It took me several rewinds to understand that from my professor and I got it in 3 mins with the way you explained and visualized it! Thank you!
Clear explainantion of the Ridge() model. Very intuitive. SUBSCRIBED.
the best explanation for the ridge regression I have ever listen
Great video for people like me who are beginners and don't want to go deep in the Statistics part of it but a simple explanation for data science. 🧡 from India.
Thank you for explaining bias and variance and not just moving forward without the explanation!!
You explained it in simple way and with a short video. very effective
I loved this video. I've heard about "reducing the coefficient values" in so many other places, but you explained the 'why' behind this better than any of the others that I saw.
Wow , your explaination are too good, it's my first time seeing your video and i'm really satisfied
Really a pristine work, in explaining the ideas behind the concept. I found it really useful for having an overview look before dealing with all the math behind. Thanks
The best explanation I've heard on ridge regression. Straightforward and precise! Thank you very much!
Very well explained, finally i got it! Many thanks.
Perfect explanation!!
You explained it in simple way and with a short video.
Thanks, keep the good work
Very helpful! Thank you Professor!
I like how you explained that well in a 7 min video.
Thank you! This is a very helpful explanation and visualization of ridge regression.
You're very welcome!
Very good explanation. Thank you. It gives me the idea of ridge regression.
Great explanation! Thank you!
The only and first video that allowed me to understand this shit. Thanks!!
Thank you for the quick and easy to understand tutorial
Glad it was helpful!
This is a great video, thank you!
Thank you Sir! the great explanation made the concept seem so easy!
Thank you sir, it's so simple!
Wonderful explanation. Thank you.
Amazing explanation, thanks ryan
My pleasure!
You made it easy to understand. But where do you get the alpha and slope? From the testing data set? Then the testing data set becomes the training data set.
Awesome Explanation. thanks!
Glad you enjoyed it! thanks!
In ridge regression alpha never be 0 . ☺️ Easy and clear explanation
really appreciate your effort thanks for help!
Good Intuition. Contradicting in the slides whether ridge regression increase/decrease for bias and variance.
Good Explanation....
amazing explanation!
great crystal clear
Great video and great english as well, you gained a new sub
Really appreciate the tutorial, just one query, Does regularisation always reduce the slope? I mean i think it's possible for the test dataset to have more slope than training set.
Black hole here... Looking for this answer...
Regularisation minimises the sum of squared errors while also minimising the sum of squared magnitudes of the coefficients. This pushes the ridge coefficients closer to zero. But yes, if the penalty term is too small, the slope may resemble that of OLS.
So it is highly unlikely for regularisation to increase the slope than that of OLS.
Thank you for your short video. But I did not understand why we should minimize the slope. It is just a possibility and depends on test data. You may increase the slope to get minimum residuals.
Minimizing or maximizing is decided after looking at the total errors. If maximizing increases the error then we will go to minimizing the slope.
Thank you for this *great explanation*
great intro !
I'm glad you like it
what if model needs high sensitivity to dependent variable ?
Suggestion: You explained very well Ridge & Lasso Regression, make also one for Elastic Net!
excellent concept explanation.. thank you
5:01 door opens
👏👏👏👏👏👏 well explained!
Hi Ryan, Can you please do a video on Elastic Net Regression?
Thank you sir🙏🙏
Education is about pedagogy. Who teaches. Here's a good one.
Thank you!
thank you for the video. do you speak Farsi ?
It just feels like a fancy way to include your testing set into your training set, essentially making 100% of your data a trainingset. What is the difference between those?
How does increasing Lambda trem reduces the slope. We are multiplying Lambda with Slope right, which is constant?
Sir how do we know that during regularization we have to increase or decrease the slope.
But how ridge works if the variance decrease with a steeper slope?
Isn’t alpha actually lambda?
The explaination is good, but I think that your example could be better. Having 3 points in the training set and 5 points in testing set is not a good practise. Also your 3 training points will give the same line every time, so again: not the best example
your such a hater😢
The example is perfect, it is for illustration, and textbooks use the same amount for training data points, it’s better to emphasize the idea of more testing data points to show the mainstream and pattern of the data, in reality, the dataset you use will never be as much as the samples it was testing or seen on.
The 3 similar training data points are the same reason why the problem occurs, and the ideal mechanism for solving it is to deviate your model from it.