Something went wrong while using pd.crosstab! So the updated confusion matrices are as follows - At 7:50 The correct confusion matrix is 92303 14 1535 135 At 10:30 The correct confusion matrix is 93798 41 40 108 Sorry for the mistake :)

@sahubiswajit1996

5 жыл бұрын

Why we are using "random_state=12" ?

@chrislam1341

4 жыл бұрын

@@sahubiswajit1996 it is just his preference, for being able to get the same result from the randomness.

@sumitshukla3689

4 жыл бұрын

When we apply SMOTE, the number of samples doesn't changes. But as explained by you, if we are adding some synthetic samples, the training example should also increase right??

@KumarHemjeet

3 жыл бұрын

@@sahubiswajit1996 you can take any number

@elliothank2823

3 жыл бұрын

I guess it's kinda off topic but does anybody know a good site to stream new tv shows online ?

Hi Bhavesh, Very good explanation. I was particularly confused about implementing SMOTE on the main data. But I guess you're correct that we must implement SMOTE on training data. Thank You

Thanku Bhavesh❣️❣️.Bina bore kiye padhaya 👏🏻👏🏻👏🏻 excellent

Most helpful and professional video I found on SMOTE. Thanks a lot!

@bhattbhavesh91

3 жыл бұрын

I'm glad you like it

I'll come back to this video. Seems helpful!

Your handwriting is pretty. Thanks for the explanation once again. Cheers!

Lovely Explanation! Thank you!

I started watching the undersampling video for a problem and ended up watching the full series cause of how well explained they are. Gald I discovered your channel! Wish I did sooner xD

@bhattbhavesh91

3 жыл бұрын

Glad it was helpful!

Not only you explained really well the illustration were perfect for a beginner to understand what oversampling mean. Thank you:)

@bhattbhavesh91

2 жыл бұрын

Glad it was helpful!

Thanks for teaching new stuff.☺

Thank you so much for the great explanation!

@bhattbhavesh91

2 ай бұрын

Glad it was helpful!

Thanks, Bhavesh!

@bhattbhavesh91

3 жыл бұрын

Glad you enjoyed it

hi bhavesh could you please confirm in order to ensure the oversampling method doesnt reduce the accuracy of the model should we always use hyperparameter tuning or is there some other method also to undo the damage of oversampling method in logistic regression for attrition prediction

Very well explained sir!!!

Great Explanation....👏

Very well explained Thank you. Especially appreciated the explanation of nearest neighbor

Quite interesting! Thanks for the lesson.

@bhattbhavesh91

4 жыл бұрын

Glad you liked it!

Here while fitting the training dataset after tuning hyperparameters using gridsearchcv why you have used X_train and y_train and why not X_train_res and y_train_res dataset

This is very well done :) Nothing overly flashy and yet very clear.

@bhattbhavesh91

3 жыл бұрын

Glad you enjoyed it

You have no idea how helpful that was

@bhattbhavesh91

4 жыл бұрын

Thank you so much :)

Thank you ! Simple and clear explanation

@bhattbhavesh91

Жыл бұрын

Glad it was helpful!

Thanks to explain with notes help me alot

Thank you sir for giving a wonderful lecture. Can you tell me how I can put the sampling ratio as per my choice instead of 1:1 using SMOTE?

Hi, you used only two target 0 and 1 , how to do with more than two . Suppose target 1 is around 2000 , target 2 is around 200 , target 3 is around 11 and so on.

Good work bro.. thank you

I have a categorical dependent variable with 3400 records in which the distribution of 0s and 1s are 2677 and 723 respectively, Will this be considered as an imbalanced dataset ? or if I would have 1s less than 5% of the total record only then it would be considered as imbalanced. Kindly clarify the doubt

Thanks alot. You mk it so simple :) Liked n subscribed bro.

@bhattbhavesh91

3 жыл бұрын

Thanks and welcome

When I tried to set up the smote ration, getting invalid ratio parameter for SMOTE.Can u help?

Good work man! Thanks

@bhattbhavesh91

3 жыл бұрын

Glad it helped!

6:20 what library u imported before declaring SMOTE() class?

Nice explanation

If we want to normalize the data as well, should we do it before applying SMOTE?

very informative video, simple and to the point keep it up

@bhattbhavesh91

Жыл бұрын

Glad you liked it!

I have a sample of only 28. Unfortunately I don't have more sample. Will SMOTE work? Secondly, which logistic regression should be used? Sklearn or statsmodels? Both give different results. Please help.

Thank you for this video. Understood SMOTE very well. Please make videos more often and How do you explain things so effortlessly with such clarity ? Where is this clarity coming from ? Great job

@bhattbhavesh91

3 жыл бұрын

Thank you! Will do!

Looks like the weights is also not working on smote. Any alternative way to test different weights?

Hi, what do we do if we have a balanced dataset but still want to increase the number of rows

You are some DOPE shit brother and by that i mean youre really good ! explained the important stuffs like only on train set beautifully ! really great !

With SMOTE, can we achieve higher f1 in practice? I saw that f1 was around 0.72

You are great bro

Excellent explanation!

@bhattbhavesh91

Жыл бұрын

I'm glad you liked it

so the idea of opting for ratio parameter in SMOTE to be a hyperparameter is to ensure we get better results is that correct, in general is it a good option to make ratio option of SMOTE to be a hyperparameter rather then fixing it to 1

Really help

Realy thanks♥️

@bhattbhavesh91

Жыл бұрын

You're welcome 😊

kindly tell me I have 5 classes imbalanced data set. SMOTE will work for multi CLASS data set ?

Thank you sir !

@bhattbhavesh91

Жыл бұрын

Most welcome!

After generating the synthetic data in which kind of situation this data can be useful any limitation of this type of data.

shouldn’t it be generate_auc_roc_curve(pipe, X_test). If no if Bhaveshbhai you or anyone can explain pls.

Can SMOTE be used for Multi label classification dataset ? Thank you

Can u please tell how this SMOTE can be applied for streaming data- In Test then Train Framework??

Hello Sir ! Could you please describe how SMOTE technique can be used to balance data images

thank you so much - very informative video

@bhattbhavesh91

3 жыл бұрын

Glad it was helpful!

Very Good Explanation. But, can we use this method for multiclass problem? Also, does SMOTE leads to overfitting issue?

Hi Bhavesh, very nicely explained can you please tell me the literature of the following examples. thanks

Do you need to remove outliers of dataset if you SMOTE?

How we can overcame the problem of Overlapping when used SMOTE??

How do I split my data into training and testing if my data is imbalanced?

cello pointec- bachpan ki yaad dila di :)

Thank you so much Sir

@bhattbhavesh91

2 жыл бұрын

Most welcome

Perfection

in your crosstab function you have y_test[target]. What is that? why is target used to index the y_test object?

I don't understand how we infer from auc roc. What are we seeing there and what are the values plotted here.

When the final ratio came out to be 0.005, doesn't it imply that the we are going to be generating a very small number (0.005 * majority) of samples for the minority class? How will the length of minority class samples ever be equal to that of majority class?

Can we use smote to target column in data set

Well explained

@bhattbhavesh91

3 жыл бұрын

Thank you!

Thank you for this video! 2 thumbs up! Question - at 4:06 you selected KNN = 3 but I didn't see you applying that concept in the code section. Can you please elaborate on where you set KNN as 3 in the code section? Did I misunderstand something?

@IykeDx

4 ай бұрын

When KNN is not stated, the default is 5.

Can i apply sampling for test set too.. Becuase its also very unbalanced??? Plzzz reply

Nice expalnation

how does smote work with categorical data?

even i have this doubt - Hi, you used only two target 0 and 1 , how to do with more than two . Suppose target 1 is around 2000 , target 2 is around 200 , target 3 is around 11 and so on.

@TheRaviraaja

3 жыл бұрын

arxiv.org/pdf/1106.1813.pdf - check out algorithm, neighbours does matters.

Nice content! I would like to compare some techniques of oversampling.. Can you pl help me out to get the hard code of SMOTE not the packaged one..thanks

Hey, when I try using make_pipeline(SMOTE(), SVC()) it gives me an error : All intermediate steps should be transformers and implement fit and transform or be the string 'passthrough' 'SMOTE(k_neighbors=5, kind='deprecated', m_neighbors='deprecated', n_jobs=1, out_step='deprecated', random_state=None, ratio=None, sampling_strategy='auto', svm_estimator='deprecated')' (type ) doesn't what's going wrong here

@bhattbhavesh91

3 жыл бұрын

The SMOTE function has changed after I created this video! Please refer to the documentation!

if we use smote in the pipeline, is it only upsampling on training or also on testing when we call predict? Thanks

Thanks 👍

@bhattbhavesh91

3 жыл бұрын

Welcome 👍

Nice

Lovelyyyyyyy

again ROC auc curve is used ??

Thanks

can u elaborate with a random forest algorithm in google colab?

What if there are more than 2 classes? In your video Sir, there are only 2 classes.. For example, I want to make 3 classes.. How can I implemented 3 classes on python use SMOTE?? Thank you, Sir

The final ratio for the final model after Grid search CV was for SMOTE=0.0005/Does thatg imply that the ratio(Minority class/Majority class)=0.005 .?Then how is the minority class gettting oversampled to equal proportion as the majority class??

gettings errors as : __init__() got an unexpected keyword argument 'ratio' AttributeError: 'SMOTE' object has no attribute 'fit_sample'

Does smote guarantee to improve classifier performance ?

@bhattbhavesh91

5 жыл бұрын

Nope! It doesn't, it only upsamples your data by generating artificial samples! How good the model performs depends on how well your classes are apart!

I have got this error when trying to run the smote: __init__() got an unexpected keyword argument 'ratio' any clues ? Thank you

@GurunathHari

4 жыл бұрын

You must have figured it out by now. Am only a student. It has been deprecated as the video is 1 year old. try using this sm = SMOTE(random_state=42, sampling_strategy = 'minority')

@bhattbhavesh91

3 жыл бұрын

Thanks Gurunath for sharing this!

Hii bhavesh , i used ur this code of smote bt i m getting an error of ratio ie invalid parameter ratio for estimator Smote , how to resolve this

@bhattbhavesh91

4 жыл бұрын

I guess the function has changed! Do have a look at the documentation to learn more about it!

The smote ratio parameter is deprecated, my off balanced dataset sklearn classification_report is off balanced in the support column even after smoting.

@bhattbhavesh91

3 жыл бұрын

The SMOTE function has changed after I created this video! Please refer to the official documentation!

Hi~can you share the data set

Hiii, can you please tell how to use SMOTE on time series and sequential data

@bhattbhavesh91

4 жыл бұрын

you are a google search away for an answer!

Can you please share the notebook with us using google colab?

True positive is 0 in the confusion matrix(by the formula the Precision and Recall should be equal to zero) .So how did you get that great number (over 70 %)?

@bhattbhavesh91

3 жыл бұрын

Please read the pinned comment!

@kavanalipanahi3505

3 жыл бұрын

@@bhattbhavesh91 I like your videos. :)))

what is the use of defining random_state ?

@bhattbhavesh91

4 жыл бұрын

kzread.info/dash/bejne/lWZom7Ftl8zInLA.html

Sir, could you please make a video on outlier detection?

@bhattbhavesh91

5 жыл бұрын

I have already created a video on outlier detection. Link - kzread.info/dash/bejne/ZIWm0dWtZJqanLQ.html

Smote can only be used in Logistic Regression or any classification model

@bhattbhavesh91

4 жыл бұрын

any classification algorithm!

very well explained sir thank you

@bhattbhavesh91

4 жыл бұрын

You are welcome

Please start a playlist for beginners to learn AI ,ML please

@bhattbhavesh91

9 ай бұрын

Sure!

Smote__ratio is not a parameter of smote help me out plz......

@bhattbhavesh91

3 жыл бұрын

The SMOTE function has changed after I created this video! Please refer to the official documentation!

Getting an error: ValueError: Unknown label type: 'continuous-multioutput'

@bhattbhavesh91

4 жыл бұрын

you are a google search away for an answer!

@harishshanmugamdhanasekar311

3 жыл бұрын

@@bhattbhavesh91 lol that's right 😂

How to handled extremely imbalanced data for regression problem .

At the end of the video, how all the 4 metrics scored above 70% if the model did not predicted correct none of samples classified as 1? There was 0 True Positives and 63 False Negatives!