Mutual Information, Clearly Explained!!!
Вставка
- Опубліковано 13 чер 2024
- Mutual Information is metric that quantifies how similar or different two variables are. This is a lot like R-squared, but R-squared only works for continuous variables. What's cool about Mutual Information is that it works for both continuous and discrete variables. So, in this video, we walk you through how to calculate Mutual Information step-by-step. BAM!
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0:00 Awesome song and introduction
2:39 Joint and Marginal Probabilities
6:19 Calculating the Mutual Information for Discrete Variables
13:00 Calculating the Mutual Information for Continuous Variables
14:10 Understanding Mutual Information as a way to relate the Entropy of two variables.
#StatQuest #MutualInformation #DubbedWithAloud
To learn more about one common way to create histograms of continuous variables, see: journals.plos.org/plosone/article?id=10.1371/journal.pone.0087357
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Thank u daddy stat quest for carrying me through my university course
Ha! :)
I am binge-watching this series. Very clear and concise explanations for every topics given in the most interesting way!
Glad you like them!
Same here!
Super! I have been struggled between copula, mutual information, etc. for a while, that is exactly what I am looking for! Thank you, Josh! This video is really helpful!
Glad it was helpful!
Awesome stuff, Josh. Thank you!
My pleasure!
Your explanations are awesome!
Glad you like them!
Superb explanation! Your channel is great!
Glad you think so!
I love this video. Simple and clear.
Thanks!
Thank youuuu. you explain everything clearly
Glad it was helpful!
Mutual information, clearly explained? More like "Magnificent demonstration, you deserve more fame!" 👍
Thanks! 😃
OMG i never see this channel, how many hours would be saveeddd.. new subs here, thanks alottt for ur vids
Welcome!
Great stuff. As always.
Thank you very much! :)
An interesting explanation and nice sence of humor 👍
Thank you!
Great explanation, thank you! ❤🔥
Glad it was helpful!
Amazing as always!!!
Thank you!
Great job! Love it!
Liking my own comment to double like your video :)
Double bam! :)
you are the best god sent really stay blessed
Thank you!
Thank you so mcuh! It is really helpful. I really hope you can explain KL divergence in the next video.
I'll keep that in mind.
Love it, thanks!
Thank you!
Thank you for being a content creator
Thanks!
Not just a creator of any content either. A creator of *exceptional* content!
Keep it up. Great content
Thank you!
I love this channel
BAM! :)
@@statquest lol, very on-brand too.
Entropy === The expectation of the surprise!!! I'll never look at this concept the same again
bam! :)
Dude, you don't even know me, and I don't really know you either, but oh boyy, I fucking love you. Thank you. One day I will teach people just like you do.
Thanks! :)
awesome!!! Now waiting for a video on Chi2 Test of Independence.
I'll keep that in mind.
You got a like just for the musical numbers!
bam!
Two sigmas are like two for loops, such that, for every index of outer Sigma, the inner sigmaales a complete iteration.
bam!
Josh, thank you for the awesome easily digestible video. One question. Is there any specific guideline about binning the continuous variable? I'm fairly certain that depending on how you split it (how many bins you choose and how spread they are) the result might be different.
To learn more about one common way to create histograms of continuous variables, see: journals.plos.org/plosone/article?id=10.1371/journal.pone.0087357
@@statquest Josh, thank you for the link, but I guess I formulated my question incorrectly. The question was about not creating the histogram but actually choosing the bins. You split your set in 3 bins. Why 3? Why not 4 or 5? Would the result change drastically if you split in 5 bins? What if the distribution of the variable you are splitting is not normal or uniform? Etc
@@wowZhenek When building a histogram, choosing the bins is the hard part, and that is what that article describes - a special way to choose the number and width of bins specifically for Mutual Information. So take a look. Also, because we are using a histogram approach, it doesn't matter what the underlying distribution is. The histogram doesn't make any assumptions.
@@statquest oh, yeah, I didn't look inside the URL you gave because your described it as "one common way to create histograms of continuous variables" which seemed very much distant from what I was actually asking about. Now that I checked the link, damn, what a comprehensive abstract. Thank you very much!
Amazing as always! Any update on the transformer video?
Still working on it.
Just started Learning ML, am assured now that the journey would be smooth with this channel
Good luck! :)
Excellent content as always!
Much appreciated!
Fire🔥🔥🔥
BAM! :)
you are a genius
:)
Que Top. Dublado em português
Muito obrigado! :)
awesome
Thanks!
thankss joshh 😍😍 in 1:30 since the response variable is not continuous and takes on 0 or 1(yes/no) can we model it with logistic regression?
Yep!
Your explanations are alway awesome! I wonder how to explain Normalized Mutual Information?
I believe it's just a normalized version of mutual information (so scale it to be a value between 0 and 1).
This is great! Do you know if you can interpret a NMI value in percentages, something like 7% of information overlaps, or 7% of group members overlap?
this is cool
Thanks!
Seriously though, I think the KL divergence is worth a mention here.
Mutual information appears to be the KL divergence between the actual (empirically derived) joint probability mass function, and the (empirically derived) probability mass function assuming independence.
I know that's a lot of words, but my brain can't help seeing these relationships.
One day I hope to do a video on the KL divergence.
1) based on what to choose the number of bins? Does larger number of bins gives lesser mutual information?
2) what if the label (output value) is numerical?
Thank in advance
1) Here's how a lot of people find the best number (and width) of the bins: journals.plos.org/plosone/article?id=10.1371/journal.pone.0087357
2) Then you make a histogram of the label data.
hi, what will be the base of the logarithm when calculating entropy. I believe it was mentioned in the entropy video that for 2 outputs(yes/no or heads/tails) the base of the logarithm will be two. Is there any generalization to this statement?
Unless there is a specific reason to use a specific base for the log function, we use log base 'e'.
DOUBLE BAM!!
Thanks!
Hi, thank you Josh. I have one question. Does MI score is affected by imbalanced data?
Presumably - pretty much everything is affected by imbalanced data. This is because you have a much better estimate one class and a much worse estimate for the other.
3 more things: 1- it would have been great if you could make a comparison with correlation too here, 2- discuss the minimum and maximum value of the MI, 3- the intuition of this specific formula
Thanks! I'm not really sure you can compare Mutual Information to correlation because correlation doesn't work at all with discrete data. I mention this at 1:20.
is there a good and stable way to calculate mutual information for numeric variables *where the binning is not good*, e.g. highly skewed distributions where the middle bins are very different from the edge bins?
Hmm... off the top of my head, I don't know, but I wouldn't be surprised if there was someone out there publishing research papers on this topic.
Hey, so what if our dependent variable ( here, loves troll 2) is continuous? Can we use Mutual information in that case? by binning aren't we just converting it into a categorical variable?
You could definitely try that.
Can we have videos about all the gazillion hypothesis tests available!!
I'll keep that in mind.
Off topic question...but will chatgpt replace us as data scientists/analysts/ statisticians. I just discovered it tonight and it blew me away. I basically learned html and css in a day with it. Im worried it will massively reduce jobs in our field. I did a project that would normally take all day in a few minutes...scary stuff.
Well, if you really want his opinion, watch the AI Buzz #1 Josh uploaded three weeks ago. It’s in this channel.
As for my opinion, obviously nobody knows yet, but it will soon be a new ground-level for anybody else. For some that all they can do is basic things ChatGPT does far better, they are in danger; for others that can make more values out of ChatGPT (or any tools to come), they are in far better shape. Which do you think you and fellow data scientists are?
And even for the basic stuffs, there should be at least someone to check whether the ChatGPT has done some absurd work or not, right? Maybe at least for a few years or so.
Just out of curiosity how did you learn HTML and CSS in a day ?
And what's specific task that you solved
I didnt think ChatGPT is that impressive afterall. Makes so many mistakes is not able to do really complicated stuff. Totally overhyped!
See: ua-cam.com/video/k3b9Mvtt6lU/v-deo.html
@@statquest thank you! This is great. Im also starting my first job today post college as a research data specialist! Your videos always helped me throughout my data science bachelors, so thank you!
Can you please make a video on Latent Dirichlet Allocation
I'll keep that in mind! :)
Is it weird that my prof. gave me the mutual information equation as one that uses entropy? We were given "I(A; B) = H(B) - sum_b P(B = b) * H(A | B = b)" with no mention of the equation you showed in this video
That is odd. Mutual information can be derived from the entropy of two variables. It is the average of how the surprise in one variable is related to the surprise in another. However, this is the standard formula. See: en.wikipedia.org/wiki/Mutual_information
maybe next video on this: KL divergence
It's on the list.
6:18 not small bam, big bam... thank you very much...
BAM!!! :)
The calculation at 8:27 seems incorrect. I plugged it into a calculator and got 0.32. The log is base 2 right?
At 8:07 I say that we are using log base 'e'.
It's like FoodWishes for stats
:)
It seems information gain (defined via entropy) and mutual information are the same thing?
They are related, but not the same thing. For details, see: en.wikipedia.org/wiki/Information_gain_(decision_tree)
@@statquest Thanks, I'll check it out. And also thanks for all the videos. It's an incredible resource you've produced.
Hello, that's a great video and it has helped me understand a lot about Mutual Information as well as your other video about entropy. I do have a question.
At 11:13 the answer you get after calculation is 0.5004 and it is explained that it is close to 0.5. However when I do the math (( 4 ÷ 5 ) × log ( 5 ÷ 4 ) + ( 1 ÷ 5 ) × log( 5 ) ) the answer I get is 0.217322... Am I missing something? Because from what I understood, the closer you get to 0.5, the better it is but it is not confirmed by my other examples. Is there a maximum to mutual information?
Thank you for your video.
The problem is that you are using log base 10 instead of the natural log (log base 'e'). I talk about this at 8:07 and in this other video: ua-cam.com/video/iujLN48gumk/v-deo.html
@@statquest Thank you for your answer. That explains a lot.
I have same doubt, when both columns are equal it says mutual info is 0.5 then what is maximum value of mutual info and in which scenario ?
i'm more of a 'Goblin 3: the frolicking' man myself
bam!
What does it mean if mutual information is above 0.5? If 0.5 is perfectly shared information...
As you can see in the video, perfectly shared information can have MI > 0.5. So 0.5 is not the maximum value.
@@statquest Is MI then somehow influenced by the size of the data or the number of categories? The video seems to suggest it should be around 0.5 for perfectly shared information (at least in this example). With discrete data using 15 bins I get some values close to 1.
Thanks for these great videos.
@@Chuckmeister3 Yes, the size of the dataset matters.
@@Chuckmeister3 Interpretation from coding theory (natural log replaced by log to base 2): Mutual information I(X;Y) is the amount of bits wasted if X and Y are encoded separately instead of jointly encoded as vector (X,Y). Statement holds on average and only asymptotically, i.e. for optimal entropy coding (e.g. arithmetic encoder) with large alphabets (asymptotically for size -> oo). It's the amount of information shared by X and Y measured in bits. Mutual information can become arbitrarily large, depending on the size of the alphabets of X and Y (and the distribution p(x,y) of course). But it can't be greater than the separate entropies H(X) and H(Y), respectively the minimum of both. You can think of I(X;Y) as the intersection of H(X) and H(Y).
ps: I think the case of perfectly shared information is if there's a (bijective) function connecting each symbol of X with each symbol of Y, so that the relation between X and Y becomes deterministic. In that case H(X)=H(Y)=I(X;Y). The other extreme is X and Y being statistically independent: In that case I(X;Y) = 0.
If I want to calculate the correlation between Likes Popcorn and Likes Troll 2, can I use something like Chi2? Similarly between Height bins and Likes Troll 2. What's the advantage of calculating the Mutual Information?
The advantage is that we have a single metric that works on both continuous, discrete and mixed variables and we don't have to make any assumptions about the underlying distributions.
small bam = "bamsito"
Ha! :)
Ummm I know I have a cold right now but did anyone only hear an Italian girl speaking ?
?
If I could I'd kiss you on the mouth, wish you did a whole playlist about data compression
Ha! I'll keep that topic (data compression) in mind.
Great content. But just don't sing, you're not up to that.
Noted! :)
i will fite you if you tell daddy stat quest what to do what not to do
I was always interested how should we think if we want to invent such a technique. Imean ok, lets say I "suspect" that the probabilities here should do the job, and say my goal is to get at the end of a day some "flag" from 0 to 1 which indicates the strenght of a relationship, but how should I think on, to deside like what comes to denominator vs nominator, when use log etc. There should be something like an "thinking algorithm"
P.s
Understanding this will be very helpfull in understanding the existing fancy formulas
I talk more about the reason for the equation in my video on Entropy: ua-cam.com/video/YtebGVx-Fxw/v-deo.html
that small bam
:)