Defining problem and objectives

Before any development begins, the problem to be solved must be defined together with objectives of what good will look like, to set expectations. The way the problem is formulated is very important, as this can mean the difference between intractability and a simple solution. It is also likely to involve a conversation about where the input data for any algorithm will come from.

The typical formulation of an ML problem takes the form given X dataset, predict Y. The availability of data or lack of it thereof can affect the formulation of the problem, the solution, and its feasibility. For example, consider the problem given a large labeled set of images of handwritten digits[18], predict the label of a previously unseen image. Deep learning algorithms have demonstrated that it is possible to achieve relatively high accuracy on this particular problem with little work on the part of the engineer, as long as the training dataset is sufficiently large[19]. If the training set is not large, the problem immediately becomes more difficult and requires a careful selection of the algorithm to use. It also affects the accuracy and thus, the set of attainable objectives.

Experiments performed by Michael Nielsen on the MNIST handwritten digit dataset show that the difference between training an ML algorithm with 1 example of labeled input/output pairs per digit and 5 examples was an improvement of accuracy from around 40% to around 65% for most algorithms tested[20]. Using 10 examples per digit usually raised the accuracy a further 5%.

If insufficient data is available to meet the project objectives, it is sometimes possible to boost performance by artificially expanding the dataset by making small changes to existing examples. In the previously mentioned experiments, Nielsen observed that adding slightly rotated or translated images to the dataset improved performance by as much as 15%.