Transformations: Engineering the input and output
In the previous chapter we examined a vast array of machine learning methods: decision trees, decision rules, linear models, instance-based schemes, numeric prediction techniques, clustering algorithms, and Bayesian networks. All are sound, robust techniques that are eminently applicable to practical data mining problems.
But successful data mining involves far more than selecting a learning algorithm and running it over your data. For one thing, many learning methods have various parameters, and suitable values must be chosen for these. In most cases, results can be improved markedly by suitable choice of parameter values, and the appropriate choice depends on the data at hand. For ...