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..
00:04 L1 and L2 regularization help address overfitting in machine learning 02:12 Balancing between underfitting and overfitting is crucial for effective model training. 04:26 Regularization shrinks parameters for better prediction function 06:47 L2 regularization penalizes the overall error and leads to simpler equations. 09:14 Filtering and handling NA values in a dataset 12:02 Dropping NA values and converting categorical features into dummies for machine learning in Python. 14:28 Understanding the issues of overfitting in linear regression model 17:00 Regularization techniques like L1 and L2 improve model accuracy. 19:16 Encouraging viewers to like and share the video
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!
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.
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..
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?
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.
Great video. However, It would have been better if you had provided the justification for assigning Zeros to few NaN values and giving mean to frew records. I know "its safest to assume" butt hen I believe in real world projects we cannot just assume things.
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.
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?
Hi Sir, Thanks for all this tutorials in ML. I've tried to use this syntaxe above, but when i fit my model the score using trainning data is 0.68 whereas the reg.scores using Test data is just weird.score(X_test,Y_test) =--17761722756.9913 dummies=pd.get_dummies(df[['Suburb','Type','Method','SellerG','CouncilArea','Regionname']]) Merge=pd.concat([df,dummies],axis='columns') final=Merge.drop(['Suburb','Type','Method','SellerG','CouncilArea','Regionname'],axis='columns') final 2nd part of my question is when i use L1 and L2 Regularization the score seem correct 0.66 and 0.67 I would also mentionned that when i've used LabelEncoder i find a score test data 0.44 and Trainning data 0.42 Thanks in advance for your answers
Hey, quick update, I found out the problem in my scenario... I had filled NaN values of price with mean, which caused the problem... Now that I have dropped 'em, it's working fine... Hope you had also solved the problem (you must've, ur comment is from 2 years back XD)
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
I really love learning from your Videos, they are pretty awesome. Just a concern, as in Line 11 we ran a missing value sum code where the Price Stated, 7610 and in the next line that is Line 12, we have dropped the 7610 rows, isn't it? Also, what was the other option if we would not have dropped the valued, can we not divide the data set and treat 50 percent of the missing values in Price and as a train dataset by imputing mean, and run the test on the missing price values. I am not sure, even if this is a valid question, but I am a bit curious. Also, what was the scope for PCA here?
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.
0.01*4=0.04 , here 0.01 is lambda and 4 is theta , 0.1*4=0.4 , here same 0.1 is lmada and 4 is theta. When i increase lambda then product of lambda increase hence cost function increase not theta value at @6:47
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.
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??
Check out our premium machine learning course with 2 Industry projects: codebasics.io/courses/machine-learning-for-data-science-beginners-to-advanced
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 😂
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?
00:04 L1 and L2 regularization help address overfitting in machine learning
02:12 Balancing between underfitting and overfitting is crucial for effective model training.
04:26 Regularization shrinks parameters for better prediction function
06:47 L2 regularization penalizes the overall error and leads to simpler equations.
09:14 Filtering and handling NA values in a dataset
12:02 Dropping NA values and converting categorical features into dummies for machine learning in Python.
14:28 Understanding the issues of overfitting in linear regression model
17:00 Regularization techniques like L1 and L2 improve model accuracy.
19:16 Encouraging viewers to like and share the video
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!
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
you just said my words
Summary:
- L1 regularization helps in feature selection.
-L2 regularization helps in preventing overfitting.
Bro, you don't know how you've helped me in my computer vision journey. Thank you❤❤❤
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!
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!
Couldn't have explained it any simpler. Perfect tutorial.
Glad it helped!
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💌💌
Best tutorial on l1 and L2 Regularization.
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.
One of the best videos out there for Regularization.
Note for myself: This is the guy... his videos can clear doubts with codes.
ha ha .. thank you 🙏
thank you for helping the DS community
best learning with very good explanation. Thanks
Please do videos about XGBoost, LGBoost !! You Videos Are Pure GOLD !!
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..
I really love your content….. You change lives❤❤❤
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?
machine learning concepts and practicals made easy, Thank you so much Sir
I am happy this was helpful to you.
Kindly make video on Feature selection for Regression and classification problem
That's a really great explanation, Anyone can use this method in real use cases now. Keep it up.
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!
These are the videos we like!!!
Thanks DarkTobias. Good to see your comment.
Thank you for this video. Very straightforward and comprehensive ❤
Awesome explanation, thanks.
good explanation sir and you need appreciation , i am here .
Clear introduction. Thanks
Nice video....good lesson......funny enough i see my house address in the dataset
All your videos are totally great. Keep working on it
achine learning concepts and practicals made easy, Thank you so much Sir
You are most welcome
I really liked your way of explanation sir
thank you ! this video save my exam :)
Nice Explanation. Also Recommended to play on 2X
Good.model representation is good.hoping some deep knowledge in next video
I can understand it now, thanks to you 🥳
Excellent Tutorial, 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.
Please continue ....
Great video.
However, It would have been better if you had provided the justification for assigning Zeros to few NaN values and giving mean to frew records. I know "its safest to assume" butt hen I believe in real world projects we cannot just assume things.
Thank you vm for this video. This is straight-forward and simple to understand!
👍👍😊
Very good videos by you on each topic..thanks !!
Really great video
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.
Thank you for this video
why did you drop na value price column even though it had more than 7000 na values wont it affect the prediction??
You cannot accurately make an assumption as to what the price is based on the available data, so you have to drop it.
@@mkt4941 Thanks :)
Thanks so much sir. Great content
Thank for your video for sharing to the world.
I am glad you liked it
You are the best.
Glad it was helpful!
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 is very helpful.
Thanks so simple ❤😊
Always excellent lessons, thank you
Nice explanation
Simple but powerful😎👍
Hi Sir,
Thanks for all this tutorials in ML.
I've tried to use this syntaxe above, but when i fit my model the score using trainning data is 0.68 whereas the reg.scores using Test data is just weird.score(X_test,Y_test) =--17761722756.9913
dummies=pd.get_dummies(df[['Suburb','Type','Method','SellerG','CouncilArea','Regionname']])
Merge=pd.concat([df,dummies],axis='columns')
final=Merge.drop(['Suburb','Type','Method','SellerG','CouncilArea','Regionname'],axis='columns')
final
2nd part of my question is when i use L1 and L2 Regularization the score seem correct 0.66 and 0.67
I would also mentionned that when i've used LabelEncoder i find a score test data 0.44 and Trainning data 0.42
Thanks in advance for your answers
Same here, I really don't know what went wrong...
Hey, quick update, I found out the problem in my scenario... I had filled NaN values of price with mean, which caused the problem... Now that I have dropped 'em, it's working fine... Hope you had also solved the problem (you must've, ur comment is from 2 years back XD)
Just came across this video accidentally simply great thank you
Thank you so much teacher
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
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...
Awesom video....really awesom..
Glad you liked it
Amazing sir thank you so much
Nice example. Thank you so much!
Glad you liked it!
great video, thanks!
In L2 regularization, how can theta reduce when lambda increases, and increase when lambda decreases?
Very well explained !!
Glad it was helpful!
Cool video
👨🎓👏✔, from Brazil-Teresina-PI
Thanks Ocean. I wish you visit Brazil one day (especially Amazon rain forest :) )
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.
thank you great work
L1,L2 Regularization is valid for regression algorithm only?
good theory!
I really love learning from your Videos, they are pretty awesome.
Just a concern, as in Line 11 we ran a missing value sum code where the Price Stated, 7610 and in the next line that is Line 12, we have dropped the 7610 rows, isn't it?
Also, what was the other option if we would not have dropped the valued, can we not divide the data set and treat 50 percent of the missing values in Price and as a train dataset by imputing mean, and run the test on the missing price values.
I am not sure, even if this is a valid question, but I am a bit curious.
Also, what was the scope for PCA here?
I agree. The missing 'Price' values could have been estimated using one of the previously presented algorithms.
Thank a lot Sir❤️ Very good teaching style (theory+practical)👍
there isnt a paid course on udemy better than the information I am gaining here
Can you make a video of ensemble model of using decision tree,knn and svm code
Please make video for genetic algorithm
Hello Sir
why did you noy fill the distance parameter with mean value?
Not all superheroes wear capes!
I tried Linear Regression on the same dataset but it scored the same with Ridge and Lasso why?
Is there a specific reason why we are fill some columns with 0 while for the others we fill with the mean?
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.
is there any algorithm using which we can determine the unimportant features in our datasets?
0.01*4=0.04 , here 0.01 is lambda and 4 is theta , 0.1*4=0.4 , here same 0.1 is lmada and 4 is theta. When i increase lambda then product of lambda increase hence cost function increase not theta value at @6:47
Thankyou for this it was very useful :)
Glad it was helpful!
what about alpha value and other two parameters ?
Kindly explain Boosting algos!!
what is dual parameter and please explain what is primal formal & dual
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.
IS it ok to impute with mean such large number of records without any justification? Shouldn't the column be dropped altogether?
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
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?
So are l1 and l2 polynomial regression models?
Taking mean for prices would have been a better choice
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??
same bro
I don't know how can you explain such a simple topic in so complicated manner.
Your explanation caused more confusion than the topic itself.
Sir, i can't find link Belbourne_housing csv .