microlife

A microlife is a unit of risk representing half an hour change of life expectancy.
Introduced by David Spiegelhalter and Alejandro Leiva, microlives are intended as a simple way of communicating the impact of a lifestyle or environmental risk factor, based on the associated daily proportional effect on expected length of life. Similar to the micromort (one in a million probability of death) the microlife is intended for "rough but fair comparisons between the sizes of chronic risks". This is to avoid the biasing effects of describing risks in relative hazard ratios, converting them into somewhat tangible units. Similarly they bring long-term future risks into the here-and-now as a gain or loss of time.

micromort

A micromort is a unit of risk measuring a one-in-a-million probability of death (from micro- and mortality). Micromorts can be used to measure riskiness of various day-to-day activities. A microprobability is a one-in-a million chance of some event; thus a micromort is the microprobability of death. The micromort concept was introduced by Ronald A. Howard who pioneered the modern practice of decision analysis.

overfitting

In statistics and machine learning, overfitting occurs when a statistical model describes random error or noise instead of the underlying relationship. Overfitting generally occurs when a model is excessively complex, such as having too many parameters relative to the number of observations. A model which has been overfit will generally have poor predictive performance, as it can exaggerate minor fluctuations in the data.

The possibility of overfitting exists because the criterion used for training the model is not the same as the criterion used to judge the efficacy of a model. In particular, a model is typically trained by maximizing its performance on some set of training data. However, its efficacy is determined not by its performance on the training data but by its ability to perform well on unseen data. Overfitting occurs when a model begins to memorize training data rather than learning to generalize from trend. As an extreme example, if the number of parameters is the same as or greater than the number of observations, a simple model can learn to perfectly predict the training data simply by memorizing the training data in its entirety. Such a model will typically fail drastically on unseen data, as it has not learned to generalize at all.