Just beautiful! Thank you so much for all your effort, it is greatly appreciated!

you are doing a great work, the videos are really helpful, keep up the good work!!

@NormalizedNerd

2 жыл бұрын

Thanks a lot!

Thank you so much for the awesome explanation!

you make videos so quickly, keep up the good work!!!

@NormalizedNerd

3 жыл бұрын

Thanks, will do!

Please make a video on decision tree using grid search cv and explaining all the hyperparameters of it

Thank you.

Please make a complete series on how you create such astonishing animations to explain the concepts ...I think you use manim library of python...It would be of great help

This is a great video! I have watched all your decision tree videos, but i still wondering how to make decision tree regression or classification if you have categorical data on it?

@NormalizedNerd

3 жыл бұрын

In case of categorical variables the nodes will ask the question like this "if x1 = category 1". Instead of less than equal we'll check equality.

@kayodeoyeniran2862

Жыл бұрын

Do we need to explicitly state the equality condition for categorical feature in the code?

@BigNickPoodle

7 ай бұрын

@@kayodeoyeniran2862 For numerical features you have to check all the unique values of that feature (1,2,3,4,5...) as potential thresholds. For categorical features you do the same but for each category (color=red,color=blue,color=yellow, ...)

Thanks for the great videos. A question though. To quantify accuracy of your predictions, you use RMSE which is not a dim-less measure of error. I am just wondering about the value of RMSE normalized with mean(Yi). Thanks again.

@NormalizedNerd

3 жыл бұрын

Yeah you can surely do that.

if the max depth is too high, I get a key error during the best split routine. Is there a way to fix that?

Hello, very nice video! Is there a way to say this model r2 values is above .7 or something in decision tree regression model? if yes, how we can extract that r2 in case of your given example?

@aakashkarmakar7478

Жыл бұрын

You can calculate r2 value for any regression model by simply calculating sum of squares of explained error divided by sum of squares of total error sum of squares of total error (SST)= sum of ( y(actual) - y(mean) )^2 sum of squares of residual error (SSR)= sum of ( y(actual) - y(pred) )^2 r2 = 1 - (SSR/SST)

beside the airfoil csv, can i apply other data and apply to this model ?

why do you use variance reduction? I see other sources use sum of squared errors for splitting

can you help me convert the variance_reduction to a calculation of SSR ?

hello, I have a problem please, when I wrote the codes jupyter said " invalid syntax" !!!!, for the first codes of partition"class Node'', any help ,please

Noise is indicative of vibration.

getting error 1 frames

Where facecam?

@NormalizedNerd

3 жыл бұрын

Will do in future ;)