Multivariate Imputation By Chained Equations (MICE) algorithm for missing values | Machine Learning
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- Опубликовано: 15 июл 2024
- In this tutorial, we'll look at Multivariate Imputation By Chained Equations (MICE) algorithm, a technique by which we can effortlessly impute missing values in a dataset by looking at data from other columns and trying to estimate the best prediction for each missing value.
We'll look at the different types of missing data, viz. Missing Completely at Random (MCAR), Missing at Random (MAR) and Missing Not at Random (MNAR).
Machine Learning models can't inherently work with missing data, and hence it becomes imperative to learn how to properly decide between different kinds of imputation techniques to achieve the best possible model for our use case.
#mice #algorithm #python
Table of contents:
0:00 Intro
0:30 MCAR/ MAR/ MNAR
3:02 Problem statement
4:30 Univariate vs Multivariate imputation techniques
7:21 (finally) The MICE algorithm
I've uploaded all the relevant code and datasets used here (and all other tutorials for that matter) on my github page which is accessible here:
Link:
github.com/rachittoshniwal/ma...
Some useful resources that might be helpful for further reading:
cran.r-project.org/web/packag...
stefvanbuuren.name/fimd/sec-M...
www.ncbi.nlm.nih.gov/pmc/arti...
towardsdatascience.com/all-ab...
towardsdatascience.com/how-to...
towardsdatascience.com/uncove...
If you like my content, please do not forget to upvote this video and subscribe to my channel.
If you have any qualms regarding any of the content here, please feel free to comment below and I'll be happy to assist you in whatever capacity possible.
Thank you!
Best video on MICE so far, the name made it sound very complex but you broke it down beautifully for me. Thank you.
Thanks Rohini, appreciate it!
Thank you so much for the easy-to-understand explaination! It helps me a lot!
Great video! I'm giving a lecture on mice this week, and definitely enjoyed the way you explained the algorithm here!
Your videos are gold! You made it so easy to understand. Thank you!
Very clear explanation. Thank you!
Thank you so much for sharing this concise and straight-to-the-point tutorial. I am about to collect data for my dissertation, and I was researching how to address missing values. This video was helpful.
Amazing explanation, thank you very much!!!
One of the best video I have seen which explains MICE in such a simple and efficient way, Great work 👌.
It would be really great if you could make a video to explain MICE for categorical also, considering a scenario when both numerical and categorical missing data are involved
Great video. Thnx a lot!
Thank you so much! This helps a lot!
Thank you, much easier to understand than anything I've found so far!
Thanks!
really good video.... nice explanation ... structured and organized ... provided good references
Bunch of thanks for the clear explanation❤
Thank your very much for this great explanation
Excellent explanation!
Very well explained
wow thanks so much, your video is amazing and super helpful!
Thank you for your sharing
Thank you so much!!
Very useful video and excellent explanation.
Very good one. Thanks for upload
Awesome explanation
Wow nicely explained 👏. Thanks
Best explanation 👍👍
Nice explanation. Thanks a lot.
best explanation!!!
This is perfect. Extremely well explained, clear, concrete and easy to follow. I wish I can like this more than once.
Haha! Thanks!
Thank You for the video, this was a n excellent visual representation of the concept
Very good video for MICE
Nicely explained. Wish you a great journey ahead!
Thank you Ajay!
Thank you so much Rachit!! Very well explained! Please come up with more videos like this. Once again Thank you!!
Thanks Shubham! Appreciate it!
perfect!, this is what i was looking for
Thanks!
Thank you! Awesome video!
Thank you!
well explained 👍
AMAZING thankyou for such a clear and detailed explanation
Thanks Elizabeth, appreciate it!
Excellent
Hi Rachit Wonderfully explained. keep it up
Best Explaination! Thank you for the video..
Thanks Samira! Glad you liked it!
This video was very helpful, thanks alot Rachit.
You're welcome! I'm glad it helped!
very well explained!
Glad it was helpful!
Thank you so much for posting this video. I'm trying to figure out multiple imputation for an RCT that I just finished and it has been a confusing journey.
I'm glad it helped!
Your explanation is superb. Thanks for the video
Thanks! I'm glad it helped!
This is very clear and crisp explanation of MICE. keep it up Rachit ji.
Thank you, Arun! I'm glad it helped!
Amazing video! You have Great Content
Thank you Mr Phenomenon!
why are you so good at explaining, Like I understood literally everything, and maths was my worst subject
Wow 😂😂😂 thanks man!
Thank you for a clear, helpful video!
Thanks! I'm glad it helped!
@@rachittoshniwal There's an underlying assumption that the data in each feature are correlated, and that's why it makes sense to use MICE. Assuming that is the case (correlated features), can you give an example of when MICE would not be an appropriate strategy to use, and what other multivariate imputation methods could then be implemented?
@@heteromodal if the column to be filled up is a discrete numerical column, mice would give distorted floating point results. In that case, it'd make sense to use Predictive Mean Matching, which takes care of the discreteness
@@rachittoshniwal Thank you!
piece of art for everyone
thanks!
awesome!
Thanks Tanesha!
Great Video! Very informative. Can you please suggest how to do multiple imputations for categorical data?
Great explanation! Can you also explain how MICE selects the best predictors for a particular variable. Is is simply a pearson correlation over a certain cut off and fraction missing under a certain cut off?
1:41 that's a very culturally-specific example right there!
Nice explanation. Out of curiosity, is this similar in essence to Expectation Maximization ?
cool
Great video!
Since we used the univariate means for the initial imputations, doing multiple imputations (m = 10, m = 30, etc.) will just give us the same output "m" many times correct?
I have also seen univariate imputation refer to a situation were you are only trying to impute one column instead of multiple columns that might more than one missing value
That's the best video I've seen! Thank you so much. But in this video, the "purchased" column is ignored because this is fully observed. So what happens if missing values are only present in the "age" column, I mean the "experience", "salary" and "purchased" are fully observed and for the same reason, we will ignore them so we only have the "age" column that can not use the regression? Please help me!
Hi Rachit :)
Firstly, thank you for this tutorial. The example was very illustrative and content was lucid- made it easy to follow. I am still new to this and have a doubt. I used MICE using sklearn's IterativeImputer on one of my datasets and noticed that all my imputed values are a constant value (which makes it look more like a simple imputation). How do I approach this problem?
Thank you so much for the very clear explanation!! I am wondering what metrics we can use to determine those values converge, something like mean square error?
Thanks! I'm glad it helped!
If I understand your question correctly, missing values are unknown, so we can't say anything about the convergence really. We can however, look at the final ML model's accuracy or other metrics to see if the imputations were any good.
@@rachittoshniwal Thanks a lot for your reply! I think my question was not so clear. I was actually meant to ask what kind of metrics we can use for stopping conditions of MICE
Is there any way to find the predicted value using calcator
Hello. Is there a way to merge several imputations (for example 10), in order to have at the end a single database with the imputed variables (having taken the most present value in the 10 imputations for example) for each imputed variable ? Thanks :)
Hi, did you get your answer from somewhere else by now? ---and would like to share it with me? I think, I (we) understood multiple imputations wrong, and it isn't about merging imputated values into one dataset but its about finding the most stable imputated values in 10 different datasets and like choose one from them? I need one dataset only too, but I don't get it how....
Hi, thank you for your explanation. How do we find out the best estimator (regression,bayes,decision tree,etc) for MICE? By looking at the final ML model accuracy or is there any other way? Thank you
Hi, thanks! I'm glad it helped!
I don't think there's a definitive answer for that. It's more of trial and error really.
Hello again! :) Rewatching the video, can you mention a method or two to deal with imputation of categorical data (assuming the number of possible values per feature is way too large to use dummy variables instead)?
Hi!
There's predictive mean matching PMM for categorical data
Thank you for this video. we have to see the abs of difference matrix, Right?
Yep
Great explanation! Thank you. Also, I have to ask about the assumptions for the linear regression model.
In the case of MICE algorithms do we need to assume a certain distribution for the variables with missing values? Will the algorithm work if there are extreme values?
Thanks in advance mate!
Hi,
Since we're basically making predictions for the missing values, the LR assumptions don't matter much as they would if we were trying to gauge the impact of each predictor on the target.
( stats.stackexchange.com/questions/486672/why-dont-linear-regression-assumptions-matter-in-machine-learning )
Linear models are indeed sensitive to outliers, so they may skew the predictions a bit.
You may choose to use a tree based model as the estimator which is less sensitive to outliers
( heartbeat.fritz.ai/how-to-make-your-machine-learning-models-robust-to-outliers-44d404067d07 )
@@rachittoshniwal Thanks for your reply!! So, one can use sth Like a random forest instead of LR?
@@analisamelojete1966 yes of course,
@@rachittoshniwal Thanks mate! You’re a legend.
@@analisamelojete1966 hahaha no I'm not, but appreciate it 😂
Thanks Rachit, you are amazing.Quick Q, is there sth similar for categorical variables ??
Thanks!
and, yes : there's Predictive Mean Matching for that. stefvanbuuren.name/fimd/sec-pmm.html
Hope it helps!
@@rachittoshniwal Thank you. Will go through .
There should be a jupyter notebook for this. Line by line coding and iteration would make it more clear.
ruclips.net/video/1n7ld38PjEc/видео.html Hope it helps
How to u calculate the predicted value ...can you please tell the formulaa
Thanks for the video,
I am curious that MICE() can assign m in the function,
and by the idea you talked, we will get the exact same imputation value for every time?
There will be randomness in the case of say, a RandomForestRegressor, cuz of the random subset of features used. But you should be able to control it using the random state parameter
@@rachittoshniwal Thanks,
but why when I use PMM as the method,
MICE still provide m different complete sets? Does the results related to Gibbs sampling?
@@sam990207 in PMM we're essentially finding a set of closest neighbors of the missing data point and then randomly picking one of em, right? Quite possibly this random picking is how we get different datasets
Dont u think because of this data leakage prroblem may occurs, as we are training the data multiple time befor train test split.....???
Thanks for the excellent lecture! I do have a question. If we have features that are MAR and MCAR in the same dataset, how can we apply this technique? Should we leave the MCAR features completely out?
Hi, Hugo. I'm glad you liked it!
Well, firstly MCAR is pretty rare in nature, so on the off chance that you find one, you should technically leave that feature out as their missingness is not linked with the observed data.
@@rachittoshniwal Cool, but should we leave it in there to leverage it to build the MAR data, then after MICE is done we "unimpute" the MCAR data?
@@hugochiang6395 conceptually, we should only be looking at the MAR features to do the imputations, right? So IMO it would be improper to "use" the MCAR features in any kind of way during the imputation process ( I could be wrong though, of course)
@@rachittoshniwal Thank you!
Hi sir, is it possible to add subtitles to your video, I mean this is the best MICE video ever, but there are some words and expressions that I don't undestand.. thanks in advance
???
Hey, the video was very helpful..
Can anyone explain me while implementing MICE in RStudio we get two columns Iteration & Imputation, how can we connect that with this video.
Like in RStudio for each iteration we get 5 imputed dataset (by default). But from this video, we only get one dataset for a iteration..
It would be really helpful if anyone can explain me this. Thanks in advance
what are the assumptions of mice alogirthm? i mean when do we come to a conclusion that ,yes now we have to use MICE
Hi , Im not sure you have added Jupiter code for MICE .
Can I get MICE (based on logistic and Decision tree ) Jupiter code like you have for KNN imputer ?
Hi Sukumar,
Although sklearn does have a MICE implementation in the form of IterativeImputer, this estimator is still in experimental phase as of today. ( scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html )
It says that the API might change without any deprecation cycle.
Hence I've stayed away from implementing it in Python for now.
If you use R as well, the mice package there is fully functional. So there's that!
Hi Rachit,
Thanks for quick response , but i think we have package fancyimpute which does the MICE imputation. Let me know whether my understanding is correct.
Below is the link for the same.
medium.com/ibm-data-science-experience/missing-data-conundrum-exploration-and-imputation-techniques-9f40abe0fd87
@@kumar707ful
Hi,
fancyimpute's version has been merged into sklearn.
pypi.org/project/fancyimpute/
Hi Sukumar,
The python implementation is live now: ruclips.net/video/1n7ld38PjEc/видео.html
Let me know if you like it (or not!)
So can MICE deal with MNAR data? See Schafer & Graham 2002
for different opinions. And thank you for the video!!
Hi, thanks for liking the video!
No, MICE assumes data is MAR.
I looked at the paper, it is very informative, thanks for sharing! :)
Would outliers influence the accuracy of imputed values?
Yes of course, they could very well
Is it possible that we can view/print the complete dataset of all the iterations it makes ?. Please share the function by which we can view/print it all.
If you are using RStudio & MiCE package, the functions are:
In case you want to the imputation to be be stacked in 'long' format, use - complete(mice(data), "long")
In case u want it to stack in 'wide' format, use - complete(mice(data), "broad")
Very nice video :) But in real time how we would know data is missing at random or not?
Thanks Abraham!
First off, MCAR is very rare, so we can put it away for the time being.
For MNAR, we'd have to check the data if we see any pattern of missingness - for eg. imagine in a "calories intake" dataset, one field is whether the person is vegetarian, and another is "how many eggs they eat in a day". If a person marks himself vegetarian, the eggs column will be NaN for him (if we assume 0 is not an option to input).
I hope it helps
@@rachittoshniwal Thank you for the explanation :)
can MICE algorithm be applied having one single column or we do need multiple variables?
Hi David,
it indeed can be applied to just one column, however it is designed to "learn from others" really, so there's that. Nevertheless, the imputed value in such a case is just the mean of the values that are used to fit the imputer.
Is possible to use in some way MICE for categorcial features?
Yes, there's predictive mean matching (PMM) for categorical data
@@rachittoshniwal Thank You for the answer grat tutorial. I wonder if I can use keras neural network to predict missing values, of course it needs to modify loss function.
@@rachittoshniwal Can You make same example how to use multivariate missing data imputation for mixed features (numerical and categorical)? Should I encode categorical data at first?
Hi can we get a soft copy of the above algorithm .. I mean which you have explained using slides ..
You mean you want the ppt?
@@rachittoshniwal yes bro
@@pythontrainersthe542 sure! I'll upload it on my GitHub in a while.
I'll notify you when I do.
@@rachittoshniwal Thanks brother .. God bless
@@rachittoshniwal Got it brother .... Many thanks and God bless ..
Have you implemented it ? If yes, could you please provide the link to the code ?
Hi SciFi, yes I've implemented it.
ruclips.net/video/1n7ld38PjEc/видео.html
Hope it helps!
Still wondering, DID YOUR CRUSH RESPONSE OR YOU JUST IMPUTED THE VALUE?
Hahaha, I'd be lying if I said the former xD
Is MICE MNAR? as it considers true values
MICE assumes data is MAR, not MNAR. If data is MNAR, it means there is some reason behind that missingness
Can you please implement it with python
Yes, absolutely. It'll be out soon :)
Hi Farrukh,
The python implementation is live now: ruclips.net/video/1n7ld38PjEc/видео.html
Let me know if you like it (or not!)
MAR - OP found correlations IRL lol
Brother found some specific examples to explain MAR and MNAR 😅
Ifyou know the matlab code of MICE please inform me
No I'm sorry I don't
Thanks for creating great content!
Ultimate goal is to reach closer to mean computed values. Then why to waste resources in performing multiple iterations rather can't move ahead taking mean value as they seems to be good approximaters?
@Rachit Toshniwal
I just used a dataset which was "linear" in nature so that I could use linear regression and show that the method works! Real datasets will be messy and their distribution will be unknown, so we'd have to use other estimators probably to get good estimates for the missing values
MICE part is good but the missingness definitions are all wrong.
just laughing at the used examples.
Whatever helps!