Motivation: Tandem mass spectrometry provides the means tomatch mass spectrometry signal observations with the chemical entities that generated them. The technology produces signal spectra that contain information about the chemical dissociation pattern of a peptide that was forced to fragment using methods like collision-induced dissociation. The ability to predict these MS 2 signals and to understand this fragmentation process is important for sensitive high-throughput proteomics research. Results: We present a new tool called (MSPIP)-P-2 for predicting the intensity of the most important fragment ion signal peaks from a peptide sequence. (MSPIP)-P-2 pre-processes a large dataset with confident peptide-to-spectrum matches to facilitate data-driven model induction using a random forest regression learning algorithm. The intensity predictions of (MSPIP)-P-2 were evaluated on several independent evaluation sets and found to correlate significantly better with the observed fragment-ion intensities as compared with the current state-of-the-art PeptideART tool.