Tutorial 28- Ridge and Lasso Regression using Python and Sklearn

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[abhishekchatterjee]

Watch the 2nd part just now.... You're like a savior to me as I have some deadlines due tomorrow and this helped me a lot sir. Thank you very much.💯💯

[alakshendrasingh]

You have already trained the model with whole data at first then splitting it for the prediction. Nothing wrong but I don't think so its an ideal technique.

[shindepratibha]

I feel, steps for regression process can be like this:
1) split the data into train and test.
2) Use train data for cross-validation and find the parameter with min MSE.
3) Use the same parameter over test data and check for the accuracy of different models.

[ayankoley]

I'm following the 'the complete machine learning playlist' playlist, but you're step-jumping skipping many details saying 'hope you know this', love your teaching though but Can you make any good completed playlist??

[Kumar-ohjl]

I think the good example for regularization would be to show that model's accuracy on training data is excellent and accuracyon test data is bad i.e- overfitting; then we can use regularization and compare the results

[esakkiponraj.e]

A small suggestion --- better differentiate the terms - alpha (for Lassa) and lambda (for ridge). It is confusing.

[Amir-English]

Why did we split the data into train & test "after" doing fitting in lasso regression? I mean, shouldn't it be like splitting the data before fitting and creating a model? 8:34

[vishalvishwakarma]

Beautifully explained. Really Helpful. Thank you!

[kushswaroop]

How do we select the value of Aplha/Lambda, what is the ideal value

[iftekarpatel]

I m d first to to watch ur video.. Was watching ur bias n variance video... N notification came for this... Big fan of urs sir...

[lotmoretolearn-dataanalyti]

Great explanation. Very nice and simple explanation for ridge and lasso. Both theoretical and practical concept is good. Keep doing such videos.

[Emotekofficial]

Krish thank you for this video very informative. I have question though. Dont you think predicting (x_test, y_test) from the model that is trained from (x, y) would predict a memorized value? shouldn't it be accurate and realistic prediction if model is trained from x_train and y_train rather than x, y for testing purpose?

[MrPiickel]

Is the method correct?
In my understanding we should:
1. split up the data train and test.set (maybe - depends on data units - standardize before)
2. do the hyperparameter optimization on the train set with cv-> best model for training data
3. predict with best model on the test set -> realistic result for unseen data


you did:
1. hyperparameter optimization on all data using cross validation -> best model for all data
2. split up all data in training and test set
3. predict with best model on test set -> in my opinion your "best model" has already seen the data in the test set. Hence the result should not be quit realistic. What do you think?

[surajsoren]

can we use other scoring method rather than neg_mean_squared_error to solve the problem...If any please suggest...Please help me out..

[rajeshthumma]

Hi Krish, You are explained in a clear and easy manner to understand the concepts. Krish, If possible can you please explain the machine learning algorithms as like you explained as linear regression algorithm. I Mean to say, theoretical explanation of the all the important machine learning algorithms. thank you

[jordan]

Good videos. So far so good. From most of the videos, i feel inference part is missing. What can we infer from the plots ?

[pandya]

Hi.
I just want to know if I am not wrong, we need to use train_test_split method before training the data. right?
But you trained the data and then split the data into train & test, which for sure do not give us an accurate prediction on future predictions.


Please correct me if I am wrong.


Thank you.

[techbenchers]

Is deep learning playlist is complete ?if not please complete it🙏🙏

[sandipansarkar]

Superb explanation. Need to get my hands dirty in jupyter notebook. Thanks

[sinister_deamon]

your videos are really nice keep it up!