Algorithmic computation and predictive models

Once we have a high-dimensional representation of relevant data, we can commence the task of deriving a predictive function. We do this by using algorithms, which are essentially a set of preprogrammed recursive instructions that categorize and divide our high-dimensional data representation in a certain manner. These algorithms (these are most commonly clustering, classification, and regression) recursively separate our data points (that is, personality rankings per person) on the feature space into smaller groups where the data points are comparatively more alike. In this manner, we use algorithms to iteratively segment our high-dimensional feature space into smaller regions, which will eventually correspond to our output classes (ideally). Hence, we can reliably predict the output class of any future data points simply by placing them on our high-dimensional feature space and comparing them to the regions corresponding to our model's predicted output classes. Congratulations, we have a predictive model!