plot_prc | R Documentation |

Precision-Recall Curve summarize the trade-off between the true positive rate and the positive predictive value for a model. It is useful for measuring performance and comparing classificators.

Receiver Operating Characteristic Curve is a plot of the true positive rate (TPR) against the false positive rate (FPR) for the different thresholds. It is useful for measuring and comparing the accuracy of the classificators.

```
plot_prc(object, ..., nlabel = NULL)
plot_roc(object, ..., nlabel = NULL)
plotROC(object, ..., nlabel = NULL)
```

`object` |
An object of class |

`...` |
Other |

`nlabel` |
Number of cutoff points to show on the plot. Default is |

A ggplot object.

A ggplot object.

`plot_rroc, plot_rec`

```
library(DALEX)
# fit a model
model_glm <- glm(survived ~ ., family = binomial, data = titanic_imputed)
glm_audit <- audit(model_glm,
data = titanic_imputed,
y = titanic_imputed$survived)
# validate a model with auditor
eva_glm <- model_evaluation(glm_audit)
# plot results
plot_prc(eva_glm)
plot(eva_glm)
#add second model
model_glm_2 <- glm(survived ~ .-age, family = binomial, data = titanic_imputed)
glm_audit_2 <- audit(model_glm_2,
data = titanic_imputed,
y = titanic_imputed$survived,
label = "glm2")
eva_glm_2 <- model_evaluation(glm_audit_2)
plot_prc(eva_glm, eva_glm_2)
plot(eva_glm, eva_glm_2)
data(titanic_imputed, package = "DALEX")
# fit a model
model_glm <- glm(survived ~ ., family = binomial, data = titanic_imputed)
# use DALEX package to wrap up a model into explainer
glm_audit <- audit(model_glm,
data = titanic_imputed,
y = titanic_imputed$survived)
# validate a model with auditor
eva_glm <- model_evaluation(glm_audit)
# plot results
plot_roc(eva_glm)
plot(eva_glm)
#add second model
model_glm_2 <- glm(survived ~ .-age, family = binomial, data = titanic_imputed)
glm_audit_2 <- audit(model_glm_2,
data = titanic_imputed,
y = titanic_imputed$survived,
label = "glm2")
eva_glm_2 <- model_evaluation(glm_audit_2)
plot_roc(eva_glm, eva_glm_2)
plot(eva_glm, eva_glm_2)
```

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