Prediction Per Deep Learning

 Deep learning is a subject that is causing quite a stir at the moment. In prin-

ciple, it is a branch of machine learning that uses algorithms to recognise

objects, for example, and understand human speech. The technology is in

principle a revival of algorithms, that were popular from the beginnings of

AI: Neuronal networks. Neuronal networks are a simulation of the processes

in the brain whereby neurons and the specific fire patterns are imitated. The

real innovation is the layering of various neuronal networks which, in com-

bination with the essentially greater performance of current computers, led

to a quantum leap in diverse sectors of machine learning.

The classifier for the prediction learns a generic DNA on the basis of pro-

filing the successful customer relations, which is projected onto the entire

company’s assets. The prediction of the optima leads can be understood as

a ranking problem. The lead with the highest probability of a conversion

should be in first place in the sales pipeline. In principle, it can be under-

stood as a classic regression task where the probability of conversion is to

be predicted. Thus highly suitable is a gradient boosted regression tree, also

called random forest.