Partial Dependence Plots (Opening the Black Box)

[@jacob_dmn]

Man, lemme say this.. I’m learning Data Science from your channel not because i don’t know other resources, but because you were one of the most intuitive and realistic teachers i knew after a long journey searching the best resources, i love this channel pleaase keep lighting us 🙏💚

[@ritvikmath]

Glad to hear it!

[@hammasmajeed3715]

Thanks so much. I was reading the PDP from tutorial, did not understand anything, thought that this topic is really hard to understand. This 10 minute video cleared everything and made it very easy. 🤩 going to look for other videos in the channel.

[@xxshogunflames]

Ritvikmath the disclaimer at the end was the cherry on top, I definitely am excited to have this in my tool kit (especially with final projects around the corner) Thank you!

[@ginervs]

Fantastic, watched this once and understood the concept after trying to read obscure articles/study manual that only gave formulas and not real life example. Thank you!

[@micmaw7553]

Thank you very much for this video! Only someone who knows his subject and wants to share his knowledge can do that. It's very rare!!! Thank you for your generosity!!!!

[@aephidayatuloh]

Great explanation! And as a non-native English speaker, I can clearly understand in a simple way.

[@vreesje]

Very clear and concise explanation. After this video, the concept is very clear. Thanks!

[@mx555551]

hey thanks so much man. your explanation was amazing, you're a life saver!

[@MrMoore0312]

Very cool technique! Post-prediction analysis is an area I'm most def interested in seeing more of

[@szymonk.7237]

Very clearly and intuitivly explained. Thank you ! ❤️

[@ritvikmath]

Glad it was helpful!

[@MarinaRosellMurillo]

Great explanation, super clear and easy to understand! Thank you!

[@aditipawde5894]

I was scared to look at the notations and integrals used in the PDP literature. After watching your video, my reaction was 'Eureka!!'. Very well explained!

[@weydesign]

I really like your videos, everything is very easy to understand and it helped me a lot!

[@fabiolainkorea]

you are a great teacher...

[@khonghengo]

Really nice explaination. I really appriciate your work.

[@ritvikmath]

I appreciate your comment!

[@janezou8611]

This is very helpful! Thank you! Can you please do video for ICE plot?

[@seemaaswani8369]

amazing explanation

[@MartyAckerman310]

Needs to do a video on the probability of Ritvik not catching his marker after tossing it into the air.

[@keithmaly904]

nicely articulated. ty!

[@maty6091]

Amazing video. Thank you!

[@Aquila67280]

Wow, that was a great explanation! Thanks!

[@louisg3713]

Very clear video I just have a question regarding what you're saying at the end. You're talking about an alternative algorithm that takes the probability distribution of the p_hat for the plot. What is the name of this algorithm ?

[@sownikturaga800]

Can correlation of a feature with other features in the dataset cause flat line in the partial dependency plot?

[@lautarocisterna3339]

Amazing pace of teaching and explanation, thanks!! Are you planning to make a video related to shap values? Thanks again

[@ΔιονύσηςΓιαννούλησ-δ6γ]

You were so helpful ,thank you so much

[@xhinker]

clear and nice, Thank you! the video is underated.

[@PuneetBhushan93]

Which Blackboxes/algorithms use PDPs to derive predictions?

[@jamalnuman]

what is the difference between the PFI, SHAP, LIME and inherent FI?

[@lakshman587]

Thank you so much for the video!!

[@yashshah4172]

Hi, I enjoyed the video, can you please explain for continuous feature, how it works ? do we take all the different data points available on the dataset or we take all the numbers from the range in a specific interval ? Thanks.

[@daesoolee1083]

Super neat lecture

[@ritvikmath]

Glad you think so!

[@konstantinosmexis6561]

What if variable #GAMES LOST was a continuous variable instead of just taking discrete (integer) values?

[@Cath38639]

Great explanation!

[@dobaolinh5165]

Will 2-feature partial dependence plot solve the independence problem? For example, you plot average predicted values for every combinations of games won and games lost in your dataset?

[@akashbhateja7410]

Why further videos are hidden??? Please make them available!

[@experiacare1383]

Well explained! Thanks!

[@teddy5474]

Crystal clear!

[@ajithshenoy5566]

kindly create a video on SHAP

[@ritvikmath]

good suggestion!

[@PuneetBhushan93]

How did we calculate the individual predicted probabilities for the artificial data?---p-hat

[@Set_Get]

Thank you. I enjoyed it. I have a question: do we use each plot separately for prediction or do we combine the output of them to get the final prediction?

[@ritvikmath]

The interpretation of each plot is the isolated effect of each feature on the final score so typically they are used independently

[@kayae6659]

Great vid, thanks!

[@qiguosun129]

You can find a detailed explanation for each new word here, thank you!

[@Justin-uz7br]

good video

[@jamalnuman]

great thanks

[@rebbo263]

I am asking this myself for a long time - why make predictions for all observations and then average? Couldn't you just 1) train the model 2) set all variables to their mean except the one you are visualizing 3) Evaluate the function of the model at different points of the variable of interest 4) Plot ..? In that way you only compute lets say 10 points per variable instead going through all observations. Thanks!

[@ocamlmail]

Hi. Thank you for video. Is partial dependence is the same concept as en.wikipedia.org/wiki/Conditional_expectation ? Put it simply, it is visualization of conditional expexctation.