Which of the following plots of model complexity vs. RSS is most likely from TRAINING data (for a fixed data set)?

The question seems to be incomplete as there are no plots provided for comparison. However, I can tell you what the plot of model complexity vs. RSS (Residual Sum of Squares) for training data generally looks like.

1. As model complexity increases, the model becomes more flexible and starts to fit the training data more closely. This means it will capture more of the variance in the data, reducing the RSS. So, initially, as model complexity increases, RSS decreases.

2. However, if the model becomes too complex, it may start to overfit the training data. This means it's not only capturing the underlying pattern, but also the noise in the training data. While this might still reduce the RSS for the training data, it's not desirable as it will likely increase the error on new, unseen data (test data).

So, the plot of model complexity vs. RSS for training data typically decreases and can potentially flatten out or slightly increase if the model starts to overfit.

Please provide the plots or more context for a more accurate answer.