Episode #125 of the Stack Overflow podcast is here. We talk Tilde Club and mechanical keyboards. Listen now
john d

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 2018 Feb 12 awarded Popular Question 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 deleted 765 characters in body 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 added 1109 characters in body; added 81 characters in body 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? added 413 characters in body 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?