Nothing
## ----example, eval = FALSE-----------------------------------------------
# ecc(x, y) %>% predict(newdata) %>% [summary|validate]
## ----setup---------------------------------------------------------------
library(MLPUGS)
data("movies")
## ----data_head, eval = FALSE---------------------------------------------
# head(movies)
## ----formatted_data_head, echo = FALSE-----------------------------------
knitr::kable(head(movies))
## ----load_datasets-------------------------------------------------------
data("movies_train"); data("movies_test")
## ----train, eval = FALSE-------------------------------------------------
# fit <- ecc(movies_train[, -(1:3)], movies_train[1:3], 3, randomForest::randomForest,
# replace = TRUE)
## ----predict_rf, eval = FALSE--------------------------------------------
# pugs <- predict(fit, movies_test[, -(1:3)], burn.in = 500, n.iters = 1500, thin = 15,
# .f = randomForest:::predict.randomForest, type = "prob")
## ----gather_prob, eval = FALSE-------------------------------------------
# y_pred <- summary(pugs, type = "prob")
## ----compare_prob, echo = FALSE, eval = FALSE----------------------------
# rownames(y_pred) <- rownames(movies_test)
# knitr::kable(head(y_pred, 5), digits = 3)
## ----gather_class, eval = FALSE------------------------------------------
# y_pred <- summary(pugs, type = "class")
## ----compare_class, echo = FALSE, eval = FALSE---------------------------
# rownames(y_pred) <- rownames(movies_test)
# knitr::kable(head(y_pred, 5), digits = 3)
## ----echo=FALSE----------------------------------------------------------
knitr::kable(movies_test[1:5, 1:3],
caption="**Table 4**: True classifications for the first 5 movies in the test (validation) set.")
## ----eval=FALSE----------------------------------------------------------
# validate(pugs, movies_test[, 1:3])
## ----eval=FALSE,echo=FALSE-----------------------------------------------
# temp <- as.data.frame(t(validate(pugs, movies_test[, 1:3])))
# colnames(temp) <- "Measurement"
# temp <- cbind(temp, Description = c(
# "provides a steep penalty for predictions that are both confident and wrong",
# "average per-obs exact classification",
# "average per-obs classification with partial matches",
# "per-label classification with partial matches",
# "average per-example per-class total error"))
# knitr::kable(temp, digits = 4)
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