We live in a predictive world and we are certainly predictive beings. No matter if you either accept it or ignore it. There is no choice for us. In the most elementary learning process a predictive task has to be carried out. Sometime in the past, we learnt that fire burns and now we keep away from it because we unconsciously predict that if we get too close we are going to be seriously damaged.
Bearing in mind that we are predictive beings can be of great help, especially when you are still mentally young enough to follow learning disregarding your age. Our ability to predict represents a solid starting point to any decision to be made. The better our predictions, the better our decision-making. Because of that, we usually consider predictive accuracy as a measure of performance of our predictions. Predictive accuracy is only a quantitative representation of how good our predictions are in comparison to the real observed values. However, predictive accuracy is not enough in real world problems. Cost analysis needs to be taken into account and needs to be performed in a broad sense.
Let’s see an example. Assume you are the cash manager of an important company. Assume you currently hold a high amount of money in your bank account. I know this last assumption is nowadays an unlikely one but assume it just for illustration purposes. Simple cash management models consider this holding position to have a cost because of the not achieved returns of alternative investments. In order to transform this idle money into productive money but with low risk you may decide to invest this extra money in, for example, treasury bills. That’s perfect! You will not only maintain the same amount of money at the maturity date but with a small amount on top of that. However this transaction has also a cost. No problem. As long as this cost is smaller than the amount of money obtained from the investment the final result will remain positive. That’s true. But let’s go on and see what happen in our example. The following day an unexpected large payment has to be done within the next 10 days and the amount of money left in your bank account after the investment is not enough to cover this payment. If you are lucky you may sell the treasury bill without losing too much and have enough money to face the payment. Again a transaction cost will be charged to your profit and loss account. This extra cost is an example of the cost of not predicting.
No one knows if this sudden payment can be predicted or not but there is one certain fact: not predicting has an associated cost. Any cost related to the lack of prediction can be reduced by attempts to reduce uncertainty using the best techniques available. In our illustration example, cash managers can use different cash management models that allow them to reduce cost by using cash flow forecasts as a key input to the models. In general, the cost of not predicting can be viewed as the benefit not achieved or the incurred costs associated to the fact of the unavailability of a prediction system. However, there is another type of costs associated to the design and implementation of any prediction system that must be considered. The cost of different predictive approaches to any particular problem may be totally different making projects feasible or not. In most of the cases both kinds of costs are unknown but can be estimated. This cost estimation task is a good starting point in any data mining project. A model that makes quick and cheap predictions will probably have cost savings in both the task performed and the saved executive time. The next question is: what to predict?
photo credit: <a href=”http://www.flickr.com/photos/36196762@N04/4930275692″>Army Photography Contest – 2007 – FMWRC – Arts and Crafts – Eye of the Holder</a> via <a href=”http://photopin.com”>photopin</a> <a href=”https://creativecommons.org/licenses/by/2.0/”>(license)</a>