Episode #125 of the Stack Overflow podcast is here. We talk Tilde Club and mechanical keyboards. Listen now
Feb
12
awarded  Popular Question
2018
Jul
27
accepted the score to hope for when evaluating model by MAE, MSE or RMSE
Jul
27
accepted Locally Weighted Linear Regression implementation in either R or Python
Jul
27
accepted what are the correct ways of weighting linear regression model
Jul
27
revised Locally Weighted Linear Regression implementation in either R or Python
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Jul
26
asked what are the correct ways of weighting linear regression model
Jul
26
comment Locally Weighted Linear Regression implementation in either R or Python
thank you so much, really helpful links especially your blog post was gave great intuition and examples but when i tried to implement this i came across some problems. i updated my question i would really appreciate it if you can help with the problems.
Jul
26
revised Locally Weighted Linear Regression implementation in either R or Python
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Jul
24
comment Locally Weighted Linear Regression implementation in either R or Python
thank you, GAM seems to be a very good alternative, but still its peculiar that for lowess algorithm which at least in theory seems like a very effective model there is not one complete and comprehensive implementation available for it.
Jul
23
asked Locally Weighted Linear Regression implementation in either R or Python
Jul
15
accepted problem with with data transformation
Jul
14
asked the score to hope for when evaluating model by MAE, MSE or RMSE
Jul
13
comment understanding cross validation approach for evaluation and model selection
but nested cross validation is a combination of both itself and i am testing for different parameters but in nested cross validation with 10 fold i would have 10 different hyper-parameters for each algorithm and it is my understanding that i shouldn't use any of those, so i thought i should get the best one with another grid search after wards.
Jul
13
asked understanding cross validation approach for evaluation and model selection
Jul
12
awarded  Commentator
Jul
12
revised is it a good practice to use K-Fold cross validation instead of training, validation and test set?
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Jul
12
comment is it a good practice to use K-Fold cross validation instead of training, validation and test set?
thank you for your comprehensive answer, i would really appreciate it if you can answer me this too. say i did two random splits for train, dev and test set, now after training, tuning and all the other steps, for one of the splits i would get the following $r^2$s for train, dev and test set: 0.91, 0.92, 0.90 and for the other one i would get these: 0.93, 0.88, 0.85. can i use the first one that gave me the best result? is it valid? i mean depending on the split it seems my final test score would be totally different.
Jul
12
accepted is it a good practice to use K-Fold cross validation instead of training, validation and test set?
Jul
11
revised is it a good practice to use K-Fold cross validation instead of training, validation and test set?
added 109 characters in body; edited title
Jul
11
comment is it a good practice to use K-Fold cross validation instead of training, validation and test set?
i did do your suggestion and found a few splits that the model structure is not very different, but can i just use one of these splits? wouldn't that defeat the purpose of the randomness of splits?