Businesses Fail At Machine Learning; What’s The Problem?
If the title sounded like something affiliated with the apocalypse, then it’s probably to show just how much problem we have noticed. In as much as there is rooting and hope for business to succeed beyond their expectations, there is the need to point out just how much they are being drawn back by a machine learning deficiency, and why. According to reports, many data science projects have reached closed down before they even got to open. Businesses fail at machine learning due to many problems, and these revelations will help you sidestep the pitfalls.
As is obtainable in nearly all other life aspects, if you ask the wrong questions, you will get the wrong answers. A stark example is what happens in the finance industry and the issue of fraud identification. The first questions that come to mind would ask if it’s a specific transaction fraud or simply some technical glitch. For such determination to be made, one will need a dataset which contains examples of both fraudulent and non-fraudulent transactions. But, the real question should be if the transaction is anomalous or not. Being that most fraud detection systems depend on humans for analysis and prediction, the adverse side effect is that the approach will develop more false positives.
Using iffy data can culminate in bad predictions even if you’re using the best models. During supervised learning, the data used is the one that was previously labelled. In most events, such labelling is done by a human, which is often not so much free of errors. For a hypothetical instance that is more abnormal than usual, it’s having a model that always has ideal accuracy, but lives on inaccurate data. In order for machine learning to be able to work and succeed, one has to have the right set of data. It could be difficult for the model to produce decent results notwithstanding the good looks of the model.
Some projects, such as the one in the life sciences domain have one common problem, – the executors have to run into the issue of not being able to obtain data at any price. Well, for one, the life sciences industry is quite sensitive about storing and transferring protected health information, making a significant number of datasets available scrub this information out. In more cases than one, such information would have been relevant and would do well to better model results. For instance, if your location probably has a statistically substantial impact on their health. To make things clearer, someone from the Mississippi may have a higher chance of diabetes than someone from Connecticut.
In machine learning, it is essential to evaluate the performance of a trained model accurately. It is just as critical to measure just how well the model can perform against the training data and the test data. Such information is valuable in selecting the model to be used, the hyper-meter selection and also to determine whether or not the model is ready for production usage or not. Choose the best evaluation metrics for the current tasks in order to measure the performance. Because there’s a plethora of literature about metric selection, so no business has to wade into the depths of this topic, but there are suitable parameters that can be taken into consideration to make it work better.