Prediction Algorithms for Data Mining
In support of the data mining process, VisMiner implements algorithms for prediction modeling. It supports modelers both for classification (predicting nominal or class values) and regression (predicting continuous numeric values). In this chapter we introduce the basic algorithms implemented by VisMiner. These include decision trees, support vector machines, and artificial neural networks for classification and artificial neural networks for regression.
For the most part, the algorithms of VisMiner are a black box. One does not need to know precisely how the algorithms work in order to deploy them in data mining exercises. Consequently, this chapter may be skipped. However, knowledge of the algorithms can help in the following ways:
- Algorithm selection – each algorithm has its strengths and weaknesses. An understanding of the internal workings of an algorithm leads to a better appreciation of its strengths and weaknesses. Consequently it results in better decision making when it comes to algorithm selection as dictated by the dataset characteristics and data mining objective.
- Results evaluation – knowing how the algorithm arrived at its results helps in assessment of the applicability and confidence in the results. For example, with respect to a decision tree, how does a root level split variable compare in importance to a leaf level split?
There are issues in the application of all algorithms implemented by VisMiner. When choosing an ...