Description Usage Arguments Author(s) References See Also Examples

By taking predicted values, actual values, and measures of the risk associated with each case, generate a summary that groups the distinct predicted values, calculating the accumulative percentage Caseload, Recall, Risk, Precision, and Measure.

1 | ```
evaluateRisk(predicted, actual, risks)
``` |

`predicted` |
a numeric vector of probabilities (between 0 and 1) representing the probability of each entity being a 1. |

`actual` |
a numeric vector of classes (0 or 1). |

`risks` |
a numeric vector of risk (e.g., dollar amounts) associated with each entity that has a acutal of 1. |

Package home page: https://rattle.togaware.com

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | ```
## simulate the data that is typical in data mining
## we often have only a small number of positive known case
cases <- 1000
actual <- as.integer(rnorm(cases) > 1)
adjusted <- sum(actual)
nfa <- cases - adjusted
## risks might be dollar values associated adjusted cases
risks <- rep(0, cases)
risks[actual==1] <- round(abs(rnorm(adjusted, 10000, 5000)), 2)
## our models will generated a probability of a case being a 1
predicted <- rep(0.1, cases)
predicted[actual==1] <- predicted[actual==1] + rnorm(adjusted, 0.3, 0.1)
predicted[actual==0] <- predicted[actual==0] + rnorm(nfa, 0.1, 0.08)
predicted <- signif(predicted)
## call upon evaluateRisk to generate performance summary
ev <- evaluateRisk(predicted, actual, risks)
## have a look at the first few and last few
head(ev)
tail(ev)
## the performance is usually presented as a Risk Chart
## under the CRAN MS/Windows this causes a problem, so don't run for now
## Not run: plotRisk(ev$Caseload, ev$Precision, ev$Recall, ev$Risk)
``` |

rattle documentation built on Aug. 17, 2018, 5:04 p.m.

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