Live Day 2- Discussing Ridge, Lasso And Logistic Regression Machine Learning Algorithms 

Linear Ridge, Lasso And Logistic Regression: ------------------------------------------------------------------------- Part I: ------------------------------------------------------------------------- Agenda for the day: 1:47 Previous session recap: 6:03 Cost function: 6:25 7:47 Regression example: 7:20 Training data: 8:25 9:02 Overfitting: 9:13 10:30 Low bias and high variance: 11:45 19:17 Underfitting: 12:05 High bias and high variance: 13:45 19:30 Overfittting and underfitting scenarios: 18:20 Ridge and Lasso Regression situation: 22:00 22:30 Ridge Example: 25:38 29:50 Hyper parameters: 30:00 Lasso Regression: 32:44 36:00 (uses) Feature selection: 35:20 Cross validation: 37:00 Quick summary: 37:33 38:37 (ridge) 39:40 (lasso) 40:16 (purpose of lasso) Assumptions of Linear Regression: 46:30 ------------------------------------------------------------------------- Part II: ------------------------------------------------------------------------- Logistic Regression: 47:35 48:10 50:00(scenario) Why not Linear Regression? : 53:15 57:28 Squash: 59:00 Sigmoid function: 59:39 1:01:51 Assumptions: 1:02:44 Cost function: 1:09:38 1:15:00 1:16:15 1:19:20 Convex and Non-convex function: 1:10:45 Logistic regression algorithm: 1:22:00 Confusion Matrix: 1:29:50 Accuracy: 1:31:39 Imbalance dataset: 1:33:28 Precision and recall: 1:37:00 1:37:45 1:45:00 F score: 1:46:43 1:47:46(F 0.5 score) 1:48:38(F 2 score)

Super explanation of Ridge regression. Fundamentally its to prevent overfitting. Because cost is getting non zero. Algorithm tries to optimize the slope value. Ek teer do nishan Prevent overfit and slope is optimized due to new line

very comprehensive and amazing teaching sir. I can't thank you enough

ML 1 st session has 247K views.....But this 2 nd session has only 34K only. That is very bad. Peoples always loved to start anything. But after that they hate to continue those things. They didn't hold it. That's why peoples don't get that much of job offers and fail on interviews.

Well explained in simple way sir🙏

Thanks man ! god bless you

amazing lecture,, can you explain gzlm linkage function in details .. i feel talking abouyt range of y and mx+c after conversion will help

Pls make similar live videos or recorded videos in basics of time series forecasting explaining all the concepts.

Great work! Krish

awesome sir really i wanna say thanks for this information in crisp manner thanks so much

Thanks

thank you so much, this detailed structured videos are very helpful.

awesome session.. thank you

thank you krish i am watching your ml algorithms again and again to make better

Excellent video

Thank you sir.

@krish naik gone through multiple sites , and observing underfitting is High bias and low variance .

sir, you are great.

Hi Krish, Is the below steps was correct for regression problem. 1. In linear Regression Model first we will do EDA, Feature Engineering, Data Pre-processing and will split data into Train and Test. 2. Creating model using Linear Regression and evaluate the model like finding Loss and R2 Square. 3. If we could see more Loss then we have to do optimization using gradient decent and stochastic gradient decent for minimizing the Loss 4. Finally we have to check Bias and Variance trade-off if model getting overfitting then use L1 regularisation for preventing overfitting and L2 regularisation for preventing overfitting and feature selection as well. Thanks,

Thanks man

I think after your 7 days series on ML , DL, EDA, time series, we can participate in kaggle competition. This would be the most efficient way to learn data science ! Hope you can do the series for DL and EDA too !

Normal distribution of features is not an assumption of Linear Regression. We want normal distribution to avoid overfitting by outliers.

Lovely one..

Superb explanation sir wonderful 😊

many thanks sir many thanks

thank you, sir,

finished watching

#Thanks Sir

When I read about Linear Regression, I always see mentioned Ordinary Least Square as the most used algorithm to find the thetas parameters. Why didn't Krish mention it? Is it not important? Can anyone explain?

now I need a pepto bismol after looking at the eqns

Great

Please Cover Coding along with tutorial

@Krish Naik Sir i am not able to find this content uploaded in mega community course. Please let me know how can i get these slides.

You are the Guru........🙏🙏🙏🙏🙏 #KingKrish

1:10:01 ,Do we get convex function because of cost function or Becuase of sigmoid

If we square the less significant coefficients then it would be much better as the square value would reduce it further then according to this particular scenario ridge is better right

00:27 The main topics of discussion are ridge and lasso regression, logistic regression, and the confusion matrix. 08:25 Overfitting and underfitting are two conditions that affect model accuracy. 22:28 L2 regularization adds a unique parameter or another sample value to minimize the cost function. 27:53 Ridge regularization is used to prevent overfitting by creating a generalized model. 39:15 Preventing overfitting and feature selection are the key purposes of ridge and lasso regression. 45:08 Logistic regression is a classification algorithm. 56:09 Logistic regression is used for binary classification problems with a decision boundary. 1:01:56 Logistic regression is used to create a sigmoid curve that helps in binary classification 1:13:03 Logistic regression cost function has specific equations for y=1 and y=0. 1:18:35 Logistic regression cost function and convergence algorithm 1:31:22 Calculation of basic accuracy and imbalanced data 1:37:06 The main aim of recall is to identify true positives. 1:48:48 F-score is calculated based on the value of beta Crafted by Merlin AI.

there was a small mistake in the explanation for lasso or L1 regression we are suppose to sum the mod of the slope not the mod of sum of slopes. both are different in video you wrote | theta0 + theta1 + theta2 + theta3 + theta4 + ... + theta_n | but in actual the L1 norm should be |theta0|+|theta1|+ |theta2| + |theta3| + ...+ |theta_n| hope u get my point Thank you

Low Bias, High Variance (Overfitting): When a model has low bias and high variance, it means that the model is able to fit the training data very well (low bias), but it is overly sensitive to the specific training examples and may not generalize well to new, unseen data (high variance). Overfitting is characterized by capturing noise or random fluctuations in the training data. To find an optimal model, there is a trade-off between bias and variance. The goal is to strike a balance that minimizes both bias and variance, leading to a model that generalizes well to new data. Techniques such as regularization and cross-validation are commonly used to address overfitting and find a suitable compromise between bias and variance.

Sir very very nice sir

bro please explain in terms of vectors and getting solutions of this eqs in vector

Sir, Underfitting means High Bias and Low variance

when high Bias and High variance then predictions will be inconsistent and not accurate, Low bias and Low variance is an Ideal Model always.. Low Bias High Variance: Over fitting High Bias Low Variance :Under fitting

Can u post a video on cooks distance and leverage

There is big myth that normality assumption is for dependent feature But reality is Normality assumption is for residual (error) not for features Because if residual follow normal then its sum follow chisqure and then and then only ratio of msr/mse will follow f distribution

In logistic Regression , Our dependent feature may depend on multiple independent features at that time how can I deal with this???Thank you

Hi Krish ,please explain how slopes becomes 0 in case of Lasso

Does anybody have the materials for these live sessions? I tried to find them on the link that's provided but that isn't working.

Hi Krish, you have taught much better than Sudhansu.

Please give an example of Lasso Regression

Please don't confuse learners, model should follow normal distribution is wrong. It is "Residuals should have normal distrbution". In linear regression errors are assumed to follow normal a normal distribution with a mean of zero.

sir, notes are not available in given link. it seems invalid link. Please provide it for practice.

Anyone Can you please post the Notes over here. I'm unable to open the link. As it got expired.

1:02 ,what is g(z) here Krish ,is it predicted variable y

Under fitting means high bias and low variance. Please correct it

Great session! some1 please help. I am unable to download material

Please give the link for the notebook

anybody has notes of this course, would be very helpful if someone can share them, or tell where to access them.

Plz Update the study materials.

Can I know about live projects when it is starting????

Notes are not available on community

Sir where can i get this PDF.

Are these for freshers ....?

Hi Krish, Are the materials available even now ? How do I download ?

Hy sir, my dataset containing 297 features and 9 types of prediction and results with Logistic regressions are low, why is it not a binary formate outcome so results are poor????

i am unable to get the material

High bias and low variance : For Underfitting : 14:26 min

Hi Krish, I am not able to get into community forum to get this pdf file which you have written during the course. Are the documents removed from community forum.

41:52 Assumptions of LR

In spam classification why we use precision

the notes link is not working

Where are these notes

can someone share the PDF of this series

linear regression

Please arrange a coding session for mL

Overfiting and underfiting use

just have a little doubt here :, 41:00 WHY WE DIDNT DIVIDE THE COST FUNCTION BY 2m?

Hi guys, asking this for a requirement I’m working on, how to reduce the false positives in my model? I’m getting 1700 positive predictions out of which the actual positives is 46. It would be great if someone help me. Thanks in advance!

I complete my boards Can i join is it relevant to me?

14:32 Correction.. Underfitting occurs if the model or algorithm shows low variance but high bias (to contrast the opposite, overfitting from high variance and low bias). I

Assumption of linear regression Linearity normality of error Independence of error No autocorrelation Homoscedasticity residual variance equal and mean of residual equal to 0

Overfitting: Good performance on the training data, poor generliazation to other data (low bias but high variance). Underfitting: Poor performance on the training data and poor generalization to other data ( high bias and high variance).

Most important part 1:29:00

Just published by @Krish Naik, new video describing Lasso and ElasticNet: ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-qbJKrlOxlJA.html - with helpful numerical examples of how feature selection works in Lasso.

I see no reason the (h0(x) - y)^2 for logistic regression is non-convex. 🧐

1:41:51 / 1:52:40

Sir hindi main bhi bata sakte ho kya

Sir, please update the phone numbers and the links in the description

Sir, if Logistic Regression is Classification problem then why it is called logistic regression and not logistic classification ???

I dont understand how underfitting = High bias and High variance

Please teach on white screen.

why you are making most of the videos as members only content which were free before. is it a greed for money now?

Thanks