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  2. One in ten rule - Wikipedia

    en.wikipedia.org/wiki/One_in_ten_rule

    Alternatively, three requirements for prediction model estimation have been suggested: the model should have a global shrinkage factor of ≥ .9, an absolute difference of ≤ .05 in the model's apparent and adjusted Nagelkerke R 2, and a precise estimation of the overall risk or rate in the target population. [10]

  3. Generalization error - Wikipedia

    en.wikipedia.org/wiki/Generalization_error

    The model is then trained on a training sample and evaluated on the testing sample. The testing sample is previously unseen by the algorithm and so represents a random sample from the joint probability distribution of x {\displaystyle x} and y {\displaystyle y} .

  4. Predictive modelling - Wikipedia

    en.wikipedia.org/wiki/Predictive_modelling

    For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place. [2] In many cases, the model is chosen on the basis of detection theory to try to guess the probability of an outcome given a set amount of input data, for example given an email determining how likely that it is spam.

  5. Predictive analytics - Wikipedia

    en.wikipedia.org/wiki/Predictive_analytics

    The objective of these models is to assess the possibility that a unit in another sample will display the same pattern. Predictive model solutions can be considered a type of data mining technology. The models can analyze both historical and current data and generate a model in order to predict potential future outcomes. [14]

  6. Clinical prediction rule - Wikipedia

    en.wikipedia.org/wiki/Clinical_prediction_rule

    In a prediction rule study, investigators identify a consecutive group of patients who are suspected of having a specific disease or outcome. The investigators then obtain a standard set of clinical observations on each patient and a test or clinical follow-up to define the true state of the patient.

  7. Swiss cheese model - Wikipedia

    en.wikipedia.org/wiki/Swiss_cheese_model

    The Swiss cheese model of accident causation is a model used in risk analysis and risk management. It likens human systems to multiple slices of Swiss cheese , which has randomly placed and sized holes in each slice, stacked side by side, in which the risk of a threat becoming a reality is mitigated by the differing layers and types of defenses ...

  8. Probabilistic risk assessment - Wikipedia

    en.wikipedia.org/wiki/Probabilistic_risk_assessment

    Probabilistic risk assessment (PRA) is a systematic and comprehensive methodology to evaluate risks associated with a complex engineered technological entity (such as an airliner or a nuclear power plant) or the effects of stressors on the environment (probabilistic environmental risk assessment, or PERA).

  9. Threshold model - Wikipedia

    en.wikipedia.org/wiki/Threshold_model

    The liability-threshold model is frequently employed in medicine and genetics to model risk factors contributing to disease. In a genetic context, the variables are all the genes and different environmental conditions, which protect against or increase the risk of a disease, and the threshold z is the biological limit past which disease develops.