evaluateRisk | R Documentation |

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.

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

`plotRisk`

.

## 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)

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