q1: I know that Percolator used cross-validation in three splits so the table has three parts. Why each part has two rows?
One thing Percolator does is normalize the features to have a mean of zero and variance of one. Each part in the weights file contains weights on both the normalized feature scale (the first row) and the original feature scale (the second row).
value of 'dM' or 'absdM' in the red box is reached to dozens, even
hundreds. If 'absdM' means the absolute mass error, how can it be so
The weights in these rows are on the original feature scale---I would not be surprised if the values were higher than the other features, because the mass error is often very small. However, in this case it looks like the 'absdM' feature is receiving more weight than I would typically expect (-8, -9, -9 on the normalized scale). Can you provide more details about your experiment (for example, the type of instrument that was used)? I suspect that maybe the default 'mz-bin-width' of 0.02 for the tide-search command may be too small.
q3: According to the table, how
should I calculate the importance of each feature? In my mind, the
importance value is in the range of zero to one.
You can think of the normalized weights as a crude, relative measure of importance. While the normalized weights do not have any units associated with them, they indicate how much each feature contributes to Percolator's score. For example, the large, negative weight for the 'absdM' feature indicates that PSMs are heavily penalized as the absolute difference between the observed and theoretical mass increases. However, its worth noting that these weights are confounded by features that are correlated with one another. When two features are highly correlated with one another, then either feature can often substitute for the other. This can result in highly variable weights that may not reflect the actual magnitude of either feature's importance in reality.