How the Trendskout AutoML Tool finds the best model
There are plenty of algorithms available for Artificial Intelligence, Machine Learning and Deep Learning. Popular examples include neural networks, gradient descent trees and support-vector machines or SVMs. It is not always straightforward to select the right algorithm for a particular business case. Every type of algorithm – take neural networks as an example – consists of different subtypes, increasing the challenge to make the appropriate choice of algorithm.
Trendskout evaluates each and every algorithm available and selects the most suitable approach for your selected application and data set.
Selecting the right algorithm does not suffice to be successful. Algorithms need to have the right data to train themselves, make predictions and identify links. The shape of the data is of paramount importance: data processing will be essential before the algorithm can start analysing and interpreting those data. The options for data processing are virtually unlimited and include data merging, transformation, generation of derivatives and variations, and denormalisation.