Lasso Regression | Intuition and Code Sample

Lasso Regression | Intuition and Code Sample | Regularized Linear Models

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CampusX

Күн бұрын

Пікірлер: 39

@ayushuttarwar8383 3 ай бұрын

Sir your videos deserve lot more views than it has. Best content found ever !!

@jamitkumar7251 13 күн бұрын

24:48 the X - axis i think, its not alpha, its W or for the dataset its the slope 'm', the weight

@siyays1868 2 жыл бұрын

Ur videos really clears everyone's doubt. Hatts off to ur dedication.

@MWASI-kk8nn Ай бұрын

Thank you for the help sir ❤

@ParthivShah 11 ай бұрын

Thank You Sir.

@AmitDas-ll4se Ай бұрын

🙏Sir, You are my favourite 🙏

@ujjalroy1442 5 ай бұрын

Awesome sir...

@rockykumarverma980 4 ай бұрын

Thank you so much sir🙏🙏🙏

@parthshukla1025 3 жыл бұрын

Thanks A Lot Sir !!!

@morhadi Ай бұрын

Tip : The Graphs of Coeff vs Alpha or R2 vs Alpha are better visualised when Alpha is taken on a log scale.

@abhishekkukreja6735 2 жыл бұрын

Hi nitish sir, at 10:57 you said all the less impacted coefficients will be 0 but you said in ridge regression that when lambda is increased it impacts the highly impacted coefficients which then tends to infinity and so how in lasso we are able to decrease the less impacted coefficients , while increasing the lambda ???? will be looking for your reply nitish sir.

@sarveshjoshi2611 Ай бұрын

He never said it.. instead he said, in ridge, if you increase the lambda, the coefficient will tend towards zero but they never will be zero and also the higher the weightage of coefficient the more will be they affected

@jamitkumar7251 13 күн бұрын

after 4 videos of ridge we get to know lasso is preferred over ridge 🥺

@gamesden8021 Жыл бұрын

sir your videos is so interesting but my question is circle and loss function contor plot pr hamara solution mil raha hai ridge regression sy jabky woh point error hoga because woh point tu local minima yeah global minima nahi hai

@sowmyak3326 Жыл бұрын

Hi sir, thanks a lot for your videos. I really learnt a lot. But, I have a small question should we consider the scale of independent variables? Wouldn't scale have an impact on coefficients?

@ajaykushwaha-je6mw 3 жыл бұрын

Awesome video, sir ek request hai. Please ek video banaiye for Hypertuning L1 and L2 k liye so that hum best choose ker payein for both.

@campusx-official 3 жыл бұрын

Okay. Noted

@SidIndian082 2 жыл бұрын

@@campusx-official Sir Code : Understanding of Lasso Regression Key points. not able to download ..pls help

@yuktashinde3636 2 жыл бұрын

THANK YOU GURU

@near_. 2 жыл бұрын

Are you doing any project

@uditjec8587 Жыл бұрын

@25:31 r2 score is negative. but r2 score can not be negative. Then how r2 score is negative here?

@anirbanmukherjee8574 2 жыл бұрын

As per SVM discussion, lambda is inversely proportional to alpha value. So, lambda increases bias should be low as it will lead to overfitting? Please let me know, if my understanding is right or wrong?

@gamesden8021 Жыл бұрын

sir my question is ky apny previous video ma kaha tha m higher hai tu woh fastly decrese kary ga jabky is ma apny kaha ju less important columns hain it think jis ka m small hai woh fastly equal to zero ho jahain gai plz solve my douts

@rohitdahiya6697 2 жыл бұрын

why there is no learning rate hyperparameter in scikit-learn lasso/Elasticnet . As it has a hyperparameter called max_iteration that means it uses gradient descent but still there is no learning rate present in hyperparameters . if anyone knows please help me out with it.

@KN-tx7sd 2 жыл бұрын

Sir, thank you. You have described the effect of different values of Lamba on the feature selection. However, for a study with n number of features how do we know which lambda is the best no overfitting or no underfitting? Is there a standard formula/script that could be used to identify this value for lambda for any study?

@manishnayak9759 2 жыл бұрын

by cross validation technique you will get the best lamda