Tuning stock models for better performance
Some learning problems are well suited to the stock models presented in previous chapters. In such cases, it may not be necessary to spend much time iterating and refining the model; it may perform well enough as it is. On the other hand, some problems are inherently more difficult. The underlying concepts to be learned may be extremely complex, requiring an understanding of many subtle relationships, or the problem may be affected by random variation, making it difficult to define the signal within the noise.
Developing models that perform extremely well on difficult problems is every bit an art as it is a science. Sometimes a bit of intuition is helpful when trying to identify areas where performance can be improved. In other cases, finding improvements will require a brute-force, trial-and-error approach. Of course, the process of searching numerous possible improvements can be aided by the use of automated programs...
