Machine Learning Tutorial Python - 17: L1 and L2 Regularization | Lasso, Ridge Regression
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- čas přidán 21. 07. 2024
- In this Python machine learning tutorial for beginners, we will look into,
1) What is overfitting, underfitting
2) How to address overfitting using L1 and L2 regularization
3) Write code in Python and sklearn for housing price prediction where we will see a model overfit when we use simple linear regression. Then we will use Lasso regression (L1 regularization) and ridge regression (L2 regression) to address this overfitting issue
Code: github.com/codebasics/py/tree...
#MachineLearning #PythonMachineLearning #MachineLearningTutorial #Python #PythonTutorial #PythonTraining #MachineLearningCource #L1andL2Regularization #Regularization #sklearntutorials #scikitlearntutorials
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Statquest theory+Codebasics Practical implementation=😍😍😍
ha ha .. nice :) Yes I also like statquest.
Exactly!
Same😂👌
@@codebasics BAM!! :P Btw, the way you explained Yolo that was superb, bro!
Yes! Minor comment, kindly please switch age and matches won. Got confused at first 😂
I have been following all 17 videos on ML you provided so far and found this is the best resource to learn from . Thank you!
Clean, crisp and crystal clear, I was struggling to understand this from a long time, your 20 mins video cleared it in one attempt, thanks a lot💌💌
Bro, you don't know how you've helped me in my computer vision journey. Thank you❤❤❤
A good video to understand the practical implementation of L1 and L2. Thank You
Such a great video!! I was struggling to understand regularization and now it's crystal clear to me!
One of the best videos out there for Regularization.
That's a really great explanation, Anyone can use this method in real use cases now. Keep it up.
Thank you for your interesting video. As far as I get from the video, L1, L2 regulations help to overcome the overfit problem from Linear regression! What is about other algorithms ( Support vector machine, logistic regression..) , how can we overcome the overfit problem?
Sir your all the vedios are really helpful...Now Iam giving you the feed back of the vedio Iam going to see.This is also an beautiful vedio and Hyperparamter tuning also an very best vedio......God Bless you..u..work hard in getting think to understand in easy manner..
Best tutorial on l1 and L2 Regularization.
All your videos are totally great. Keep working on it
Thank you for this video. Very straightforward and comprehensive ❤
you should probably change the X and Y axes. Matches won is a function of Age. So, Age should be on X axis and Matches won on Y axis
That will more familiar. :D
familiar where !@@hansamaldharmananda9605
machine learning concepts and practicals made easy, Thank you so much Sir
I am happy this was helpful to you.
Couldn't have explained it any simpler. Perfect tutorial.
Glad it helped!
best learning with very good explanation. Thanks
I really love your content….. You change lives❤❤❤
As per the equation y = mX + c, you inter-changed the y & X axis, if I'm not wrong.
Because you are trying to predict match won(yhat) which is your horizontal line and age(X) is on vertical line.
Maybe using something unconventional mislead new-learners.
As X is a horizontal line and y is vertical line, that's what we learned since school time.
Assigning X & y to axis(as per your explanation) will be great help to learner.
I hope you are not taking personally. My opologies if so!
Just came across this video accidentally simply great thank you
Nice explanation .. Adding to that
L2 Ridge : Goal is to prevent multicollinearity and control magnitude of the coefficients
where highly corelated features can be removed by shirking the coefficients towards to zero not exactly zero , stability and generalization.
L1 Lasso : Goal is to prevent sparsity in the model by shirking the coefficients exactly to zero , importance in feature selection, preventing overfitting..
so, in what cases should we use L1 and L2?
Great tutorial sir.Its a privilege to be a fan of yours.Please sir could you please do a video on steps to carry out when doing data cleaning for big data.Thank you.
thank you a lot, I'm from Russia and I'm student. I watch your video about ML and It helps me to understand better
Glad to hear that!
on taking these parameter-: xtrain,xtest,ytrain,ytest=train_test_split(x,y,test_size=0.30,random_state=101)
i got lr.score(xtest,ytest) =0.6642052270622596
lr.score(xtrain,ytrain) =0.6819231366292379
so it doesn't seems me that much overfitting.. stll i have to do regularization??
Very good videos by you on each topic..thanks !!
Can we use Lasso for feature selection on classification problems?
Don't we have to one-hot encode Postcode, Propertycount as well since they are actually categorical values instead of continuous values?
Clear introduction. Thanks
Always excellent lessons, thank you
Kindly make video on Feature selection for Regression and classification problem
Excellent Tutorial, Thanks.
Nice video, my question is what will u do so accuracy will jump on this dataset from 67 to 90+?
IS it ok to impute with mean such large number of records without any justification? Shouldn't the column be dropped altogether?
is there any algorithm using which we can determine the unimportant features in our datasets?
Please do videos about XGBoost, LGBoost !! You Videos Are Pure GOLD !!
I really liked your way of explanation sir
Good.model representation is good.hoping some deep knowledge in next video
I tried Linear Regression on the same dataset but it scored the same with Ridge and Lasso why?
Thank you vm for this video. This is straight-forward and simple to understand!
👍👍😊
Really great video
Thank a lot Sir❤️ Very good teaching style (theory+practical)👍
Sir, is there a way to find best parameter for LASSO and Rigid regression, if yes then please create a video for the same
achine learning concepts and practicals made easy, Thank you so much Sir
You are most welcome
When I am creating dummies, it is showing that the Suburb column is of type NoneType() and no dummies are getting created. What can be the problem?
I can understand it now, thanks to you 🥳
Simple but powerful😎👍
@7:00 what does the penalizing means, can anyone explain, I'm confused with this term.
Thanks in Advance.
Very nice video sir but at first i hoped you show the plot of scatter plot of the data and how the curve of the L1/L2 regression...
Nice video....good lesson......funny enough i see my house address in the dataset
Thank you so much teacher
Can you please provide the jupyter notebook link for this piece of code sir?
Nice Explanation. Also Recommended to play on 2X
First when you apply lasso, you apply it apart from the first linear regression model you made right?
Which means applying scikit Lasso is like making a linear regression but with regularization or it is applied to the linear regresion from the cell above??
So what if I use a knn or a forest?
thank you ! this video save my exam :)
Hello Sir
why did you noy fill the distance parameter with mean value?
thank you great work
good explanation sir and you need appreciation , i am here .
Thank you. This is very helpful.
Thanks so much sir. Great content
The best of two worlds wow!
thank you for helping the DS community
Thank's for class it's very clearly for me.
But I had a problem to create a sending file my code from to Kaggle, help me please.
great video, thanks!
Hi...The equation, shouldn't it be : Theta0 + Theta1.x1 + Theta2.square (x1)+Theta3.cube (x1) rather than Theta0 + Theta1.x1 + Theta2.square (x2)+Theta3.cube (x3) because we have only one x feature ?
2) the Regularization expression (Lambda part), my understanding is that we should not take "i & n" , rather we should take "j & m" etc. The reason is that in first half of equation, we took "i & n" for number of rows whereas in second half, we need to take number of features, so different parameters should be used.
Please correct me if my understanding is wrong.
Sir, i can't find link Belbourne_housing csv .
In L2 regularization, how can theta reduce when lambda increases, and increase when lambda decreases?
Sir,I am fresher & want to make career in finance domain data analyst & I have no any experience in finance domain so how can I gain knowledge in finance domain so pls give some suggestion about it.
Note for myself: This is the guy... his videos can clear doubts with codes.
ha ha .. thank you 🙏
Nice explanation
sir can you provide ppt and jupyter notebook link of above used resources?
Amazing sir thank you so much
what about alpha value and other two parameters ?
Hey, great video thank you. Quick question - what's the best way to find the optimal alpha? Do you do a grid search?
Yes doing grid search would be a way
I believe the most appropriate imputing method here is to group by the similar type of houses and then fill with the mean value of the group. For example, if the average is, say, 90 m^2, and the home is only a flat, the building area is incorrectly imputed.
L1,L2 Regularization is valid for regression algorithm only?
Thanks so simple ❤😊
These are the videos we like!!!
Thanks DarkTobias. Good to see your comment.
good theory!
Can you make a video of ensemble model of using decision tree,knn and svm code
where can i find a the script for this lecture
I think one must not use those imputations(mean) before train test split as it leads to data leakage, correct me if I am wrong.
So are l1 and l2 polynomial regression models?
Maybe in the Cost formula, the indices for summation should be different (in general): for the MSE term the sum should be over the entire training dataset (in this case n), and the sum for the regularization term should run over the number of features or columns in the dataset
ho to ccomputer gradient of L1 reg its not even differentiable
Nice example. Thank you so much!
Glad you liked it!
something doesn't look right. How many degrees of the polynomial was fit via the ridge/lasso regression?
My lasso regression is getting wrong results. It is giving all coefficients as zero except the constant and R2 score as --0.001825328970232576. Someone please help.
Cool video
can you share melborune hosuing price here in youtube while you upload
Thank for your video for sharing to the world.
I am glad you liked it
what is dual parameter and please explain what is primal formal & dual
Appreciate the efforts, but there were issues with the foundational understanding. Additionally, the inclusion of dummy variables expanded the columns to 745 without acknowledgement or communication regarding its potential adverse effects to viewers was not expected.
from where can i download csv files
Kindly explain Boosting algos!!
Awesom video....really awesom..
Glad you liked it
thanks sir
hello sir currently i am pursuing b tech final year..I want very badly to do projects on ml . Can u plzz give me the project ideas.
Very well explained !!
Glad it was helpful!
Please make video for genetic algorithm