## 10.41 Risk Variable

20210811 We might observe that the variable risk_mm contains the amount of rain recorded tomorrow. We can treat this as a risk variable, being a measure of the impact, or the severity, of the prediction. That is, a measure of the amount of risk associated with a “positive” outcome (that it will rain tomorrow).

A risk variable is an output variable and should not be used as an input to any machine learning model building—it is not an independent variable. In other circumstances it might actually be treated as the target variable. If you do accidentally include it as an input, the variable will usually be selected as the strongest predictor of the outcome, even the perfect predictor. Give it a try. Whenever you see such a perfect model start questioning whether the variable can be used in this way.

We can explore our risk variable here:

# Note the risk variable - measures the severity of the outcome.

risk <- "risk_mm"

For this risk variable note that we expect it to have a value of 0 for all observations when the target variable has the value No.

# Review the distribution of the risk variable for non-targets.

ds %>%
filter(rain_tomorrow == "No") %>%
select(risk_mm) %>%
summary()
##     risk_mm
##  Min.   :0.0000
##  1st Qu.:0.0000
##  Median :0.0000
##  Mean   :0.0726
##  3rd Qu.:0.0000
##  Max.   :1.0000

Note that a little rain (defined as 1mm or less) is regarded as no rain. That is useful to keep in mind and is a discovery of the data that we might not have expected. As data scientists we should be expecting to find the unexpected.

A similar analysis for the target observations is more in line with expectations.

# Review the distribution of the risk variable for targets.

ds %>%
filter(rain_tomorrow == "Yes") %>%
select(risk_mm) %>%
summary()
##     risk_mm
##  Min.   :  1.10
##  1st Qu.:  2.40
##  Median :  5.00
##  Mean   : 10.17
##  3rd Qu.: 11.40
##  Max.   :474.00

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