在插入符号中设置种子平行随机森林以获得可重现的结果 [英] Set seed parallel random forest in caret for reproducible result

问题描述

我希望使用 caret 包并行运行随机森林，并且我希望设置可重现结果的种子，如 使用 caret 的完全可重现的并行模型.但是，我不明白从插入符号帮助中获取的以下代码中的第 9 行:为什么我们采样 22(加上第 12、23 行中的最后一个模型)整数(评估参数 k 的 12 个值)?有关信息，我希望运行 5 倍 CV 来评估 RF 参数mtry"的 584 个值.任何帮助深表感谢.谢谢.

I wish to run random forest in parallel using caret package, and I wish to set the seeds for reproducible result as in Fully reproducible parallel models using caret. However, I don't understand line 9 in the following code taken from caret help: why do we sample 22 (plus the last model in line 12, 23) integer numbers (12 values for parameter k are evaluated)? For information, I wish to run 5-fold CV to evaluate 584 values for RF parameter 'mtry'. Any help is much appreciated. Thank you.

## Not run:

## Do 5 repeats of 10-Fold CV for the iris data. We will fit
## a KNN model that evaluates 12 values of k and set the seed
## at each iteration.

set.seed(123)
seeds <- vector(mode = "list", length = 51)
for(i in 1:50) seeds[[i]] <- sample.int(1000, 22) # Why 22?

## For the last model:
seeds[[51]] <- sample.int(1000, 1)

ctrl <- trainControl(method = "repeatedcv", 
                 repeats = 5,
                 seeds = seeds)

推荐答案

我会说这是一个错误，应该是 12 而不是 22.

I'd say it is a mistake, and should be 12 instead of 22.

据我所知，您将运行模型 10*5 = 50 次，对于 k 的每个值.因此，对于 1:50 中的 每个 i，您将需要 12 个种子(每 k 个种子).获得最佳 k 后，您将运行最终模型.这一次，你只需要一个种子(不再重复重采样).

From what I understand, you will be running the model 10*5 = 50 times, for each value of k. Hence, for each i in 1:50, you'll need 12 seeds (one for every k). After obtaining the best k, you will run the final model. This time, you only need one seed (no more repeated resampling).

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