Lesson plan
Predictive modelling
In finance and mathematics, predictive modelling is the use of statistics to predict outcomes. Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place. The output of predictive models can come in multiple forms, such as an estimated probability of a certain event occurring (e.g. Binary regression), or a scalar response variable (e.g. Linear regression). The usage of predictive modelling in business decision making is often referred to as predictive analytics. Predictive modelling is often contrasted with causal modelling. In the former, one may be entirely satisfied to make use of indicators of, or proxies for, the outcome of interest. In the latter, one seeks to determine true cause-and-effect relationships. This distinction has given rise to a burgeoning literature in the fields of research methods and statistics and to the common statement that "correlation does not imply causation". Depending on definitional boundaries, predictive modelling is synonymous with, or largely overlapping with, the field of machine learning, as it is more commonly referred to in academic or research and development contexts.…
Article summary
In finance and mathematics, predictive modelling is the use of statistics to predict outcomes. Most often the event one wants to predict is in the future, but predictive modelling can be applied to any type of unknown event, regardless of when it occurred. For example, predictive models are often used to detect crimes and identify suspects, after the crime has taken place. The output of predictive models can come in multiple forms, such as an estimated probability of a certain event occurring (e.g. Binary regression), or a scalar response variable (e.g. Linear regression). The usage of predictive modelling in business decision making is often referred to as predictive analytics. Predictive modelling is often contrasted with causal modelling. In the former, one may be entirely satisfied to make use of indicators of, or proxies for, the outcome of interest. In the latter, one seeks to determine true cause-and-effect relationships. This distinction has given rise to a burgeoning literature in the fields of research methods and statistics and to the common statement that "correlation does not imply causation". Depending on definitional boundaries, predictive modelling is synonymous with, or largely overlapping with, the field of machine learning, as it is more commonly referred to in academic or research and development contexts.…
Key claims
- publication date: 2015 — source: Wikipedia · confidence 0.95
Discussion questions
- Summarise the article in your own words. Which single claim would you verify first, and why?
- The record states that the publication date is "2015". What evidence would count against this claim?
- Which claim in this article is most likely to be revised as the sources update? How would you check its current status?
- If two sources disagreed about one of these facts, what would you need in order to trust one over the other?
Bibliography
- Wikipedia https://en.wikipedia.org/w/index.php?oldid=1375709087
Key claims and bibliography are generated from the same tracked claims and sources as the article page.