# Introduction To Probability

2017-03-21

Probability is an important statistical and mathematical concept to understand. In simple terms – probability refers to the chances of possible outcome of an event occurring within the domain of multiple outcomes. Probability is indicated by a whole number – with 0 meaning that the outcome has no chance of occurring and 1 meaning that the outcome is certain to happen. So it is mathematically represented as P(event) = (# of outcomes in event / total # of outcomes). In addition to understanding this simple thing, we will also look at a basic example of conditional probability and independent events.

It’s a good intro to a critical topic in statistics.  If I would add one thing to this, it would be to state that probability is always conditional upon something.  It’s fair to write something as P(Event) understanding that it’s a shortcut, but in reality, it’s always P(Event | Conditions), where Conditions is the set of assumptions we made in collecting this sample.

## MAPE and Its Flaws

2019-08-22

Jan Fischer takes us through Mean Absolute Percentage Error as a measure of forecast quality: Particular small actual values bias the MAPE.If any true values are very close to zero, the corresponding absolute percentage errors will be extremely high and therefore bias the informativity of the MAPE (Hyndman & Koehler 2006). The following graph clarifies this […]

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## Calculating AUC in R

2019-08-20

Andrew Treadway shows how you can calculate Area Under the Curve in R: AUC is an important metric in machine learning for classification. It is often used as a measure of a model’s performance. In effect, AUC is a measure between 0 and 1 of a model’s performance that rank-orders predictions from a model. For […]

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March 2017
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