A prediction error is the failure of some expected event to occur. When predictions fail, humans can use metacognitive functions, examining prior predictions and failures and deciding, for example, whether there are correlations and trends, such as consistently being unable to foresee outcomes accurately in particular situations. Applying that type of knowledge can inform decisions and improve the quality of future predictions.
Predictive analytics software processes new and historical data to forecast activity, behavior and trends. The programs apply statistical analysis techniques, analytical queries and machine learning algorithms to data sets to create predictive models that quantify the likelihood of a particular event happening.
Errors are an inescapable element of predictive analytics that should also be quantified and presented along with any model, often in the form of a confidence interval that indicates how accurate its predictions are expected to be. Analysis of prediction errors from similar or previous models can help determine confidence intervals.
In artificial intelligence (AI), the analysis of prediction errors can help guide machine learning (ML), similarly to the way it does for human learning. In reinforcement learning, for example, an agent might use the goal of minimizing error feedback as a way to improve. Prediction errors, in that case, might be assigned a negative value and predicted outcomes a positive value, in which case the AI would be programmed to attempt to maximize its score. That approach to ML, sometimes known as error-driven learning, seeks to stimulate learning by approximating the human drive for mastery.