The ML X-Factor
The ML X-Factor represents the same thing as it does in an algebraic equation--the unknown. This ML X-Factor is the human judgment, the critical thought, that is applied to the machine learning model. Without it, no machine learning equation can be solved because this factor gives meaning to scored predictive models and provides the context in which they are interpreted.
The ML X-Factor ensures that machine learning remains a business intelligence tool, providing the basis for answers but not becoming the answers themselves.
If you are reading this post, it is likely that you are doing the machine learning analysis inhouse but have instead hired someone to do it. And, it is more likely that you want results presented to you in this format, PowerPoint Data Analysis Sample, rather than in the one below. Lillian Weng's explanation of a Naive Bayes model is doubtless correct and quite meaningful to a fellow statistician (How to Explain the Prediction of a Machine Learning Model?)
This is where our ML X-Factor departs from the X-Factor in Ms. Weng's model. If you are handed an interpretation of a predictive data analysis that is completely incomprehensible, it will be of little use to you. We provide intelligent business intelligence that enables you to conceptualize and visualize your data.
Download Our Machine Learning Data Analysis Sample
The X-Factors in this, as in all of our products and services, is explained. Without explainability, you have only a description of the process, not the results.
You Can Provide the Data But the Data Won't Provide the Context
Finding and preparing the data that fuels your algorithm is not for the faint of heart. While a machine learning algorithm can identify correlations, it can't understand the facts surrounding the data that might make it relevant or irrelevant.
These examples of how “context” could get in the way of developing effective machine learning solutions are provided by Tech Emergence (How to Apply Machine Learning to Business Problems):
- Predicting eCommerce customer lifetime value: An algorithm could be given data about historical customer lifetime value, without taking into account that many of the customers with the highest lifetime value were contacted via a phone outreach program that ran for over two years but failed to break even, despite generating new sales. If such a telephone follow-up program will not be part of future eCommerce sales growth, then those sales shouldn’t have been fed to the machine.
- Determining medical recovery time: Data might be provided to a machine in order to determine treatment for people with first- or second-degree burns. The machine may predict that many second-degree burn victims will need only as much time as first-degree burn victims, because it doesn’t take into account the faster and more intensive care that second-degree burn victims received historically. The context was not in the data itself, so the machine simply assumes that second degree burns heal just as fast as first degree.
- Recommending related products: A recommendation engine for an eCommerce retailer over-recommends a specific product. Researchers only discover later that this product was promoted heavily over a year ago, so historical data showed a large uptick in sales from existing buyers; however, these promotional purchases were sold more based on the “deal” and the low price, and less so by the actual related intent of the customer.
ML cannot ever become commodity. Success of ML depends strongly on the knowledge, skills and dedication of the people who do it. -
Dr. Danko Nikolic — PhD, University of Oklahoma, Data Science and BD&A, Computer Sciences Corporation