validate_pugs: Assess multi-label prediction accuracy

In MLPUGS: Multi-Label Prediction Using Gibbs Sampling (and Classifier Chains)

Description

Computes a variety of accuracy metrics for multi-label predictions.

Usage

validate_pugs(object, y)

Arguments

object	A PUGS object generated by predict.ECC.
y	A matrix of the same form as the one used with ecc.

Value

A variety of multi-label classification accuracy measurements.

Examples

x <- movies_train[, -(1:3)]
y <- movies_train[, 1:3]

model_glm <- ecc(x, y, m = 1, .f = glm.fit, family = binomial(link = "logit"))

predictions_glm <- predict(model_glm, movies_test[, -(1:3)],
.f = function(glm_fit, newdata) {

  # Credit for writing the prediction function that works
  # with objects created through glm.fit goes to Thomas Lumley
  
  eta <- as.matrix(newdata) %*% glm_fit$coef
  output <- glm_fit$family$linkinv(eta)
  colnames(output) <- "1"
  return(output)
  
}, n.iters = 10, burn.in = 0, thin = 1)

validate_pugs(predictions_glm, movies_test[, 1:3])

## Not run: 

model_c50 <- ecc(x, y, .f = C50::C5.0)
predictions_c50 <- predict(model_c50, movies_test[, -(1:3)],
                           n.iters = 10, burn.in = 0, thin = 1,
                           .f = C50::predict.C5.0, type = "prob")
validate_pugs(predictions_c50, movies_test[, 1:3])
  
model_rf <- ecc(x, y, .f = randomForest::randomForest)
predictions_rf <- predict(model_rf, movies_test[, -(1:3)],
                          n.iters = 10, burn.in = 0, thin = 1,
                          .f = function(rF, newdata){
                            randomForest:::predict.randomForest(rF, newdata, type = "prob")
                          })
validate_pugs(predictions_rf, movies_test[, 1:3])

## End(Not run)

MLPUGS documentation built on May 2, 2019, 3:49 p.m.