Predictive analysis of rare events
When you have an unbalanced data set, the algorithm is going to weight its success on each data point equally, meaning the majority class comes out as much more important than the minority class. The typical solution is to sample down the majority class until it's the same size as the minority class, and an alternate (similar) solution is to adjust the cost function so that the minority class is weighted appropriately.

See these similar questions for more:


Should I go for a 'balanced' dataset or a 'representative' dataset?
Quick Guide into training highly imbalanced data sets
What are the implications for training a tree ensemble with highly biased datasets?
Skewed multi-class data
Ratio of positive to negative sample in data set for best classification