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3 votes

List of samples that each tree in a random forest is trained on in Scikit-Learn

I don't think it is possible to get it directly but we may utilize the random seed. random_stateint, RandomState instance or None, default=None Controls both the randomness of the bootstrapping of ...
10xAI's user avatar
  • 5,644
2 votes

nnet in caret. Bootstrapping or cross-validation?

There is a mistake in your train function. It should be trControl not trainControl. By default the train function doesn't do ...
kangaroo_cliff's user avatar
1 vote

Model evaluation approach allowing manual experimentation without data leakage

Instead of splitting the data in two parts, train and test, you could split the data into more parts. Basically, every time you want to evaluate something you need data that is completely unseen.
alepfu's user avatar
  • 51
1 vote

Understanding bootstrapping in bias variance decomposition

In bootstrapping, the sampling is done with replacement. So although each sample is the same size as the original training set, it will contain some duplicated instances and omit other instances. This ...
Lynn's user avatar
  • 1,307
1 vote

List of samples that each tree in a random forest is trained on in Scikit-Learn

It is possible, actually. The answer is not too different than the one given by @10xAI, but it is not trying to exploit the order of the random seeds implicitly, since it would break for parallel ...
pixelmitch's user avatar
1 vote

Resampling train and test data in R

Try using a different seed for each loop. You can do it like this. ...
bstrain's user avatar
  • 421
1 vote

Perform bootstrapping of an ordinary linear regression model, using B=100 bootstrap resamples of my dataset, and getting RMSE

The following code will run Ordinary linear regression and ridge regression on B=100 different samples of Boston data set and calculate the RMSE on the test set for all 100 different test sets and ...
Saurabh Kansal's user avatar

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