diff --git a/tests/testthat/test_pipeop_encodepl.R b/tests/testthat/test_pipeop_encodepl.R
index 30ce1acfd..d0f6aac76 100644
--- a/tests/testthat/test_pipeop_encodepl.R
+++ b/tests/testthat/test_pipeop_encodepl.R
@@ -1,17 +1,84 @@
 context("PipeOpEncodePL")
 
-test_that("PipeOpEncodePL - basic properties", {
+test_that("PipeOpEncodePLQuantiles - basic properties", {
+  task = mlr_tasks$get("iris")
+  expect_datapreproc_pipeop_class(PipeOpEncodePLQuantiles, task = task)
+})
+
+test_that("PipeOpEncodePLQuantiles - train and predict", {
+  op = PipeOpEncodePLQuantiles$new()
+  dt = data.table(
+    target = as.factor(rep(c("A", "B"), 4)),
+    x = c(2, 8, 4, 10, 6, 12, NA, NA),
+    y = c(NA, NA, 10, 25, 40, 60, 65, 95)
+  )
+  task = TaskClassif$new(id = "test", backend = dt, target = "target")
+
+  train_out = op$train(list(task))[[1L]]
+  predict_out = op$predict(list(task))[[1L]]
+
+  # Test that bins are correct
+  expect_equal(op$state$bins, list(x = c(2, 7, 12), y = c(10, 50, 95)))
+
+  # Test encoding and column naming
+  dt_encoded = data.table(
+    x.bin1 = c(0, 1, 2/5, 1, 4/5, 1, NA, NA),
+    x.bin2 = c(0, 1/5, 0, 3/5, 0, 1, NA, NA),
+    y.bin1 = c(NA, NA, 0, 15/40, 3/4, 1, 1, 1),
+    y.bin2 = c(NA, NA, 0, 0, 0, 10/45, 15/45, 1)
+  )
+  expect_equal(train_out$data(cols = train_out$feature_names), dt_encoded)
+  expect_equal(predict_out$data(cols = predict_out$feature_names), dt_encoded)
+
+  # Changed param values
+  op$param_set$set_values(numsplits = 3, type = 1)
+
+  train_out = op$train(list(task))[[1L]]
+  predict_out = op$predict(list(task))[[1L]]
+
+  # Test that bins are correct
+  expect_equal(op$state$bins, list(x = c(2, 4, 8, 12), y = c(10, 25, 60, 95)))
+
+  # Test encoding and column naming
+  dt_encoded = data.table(
+    x.bin1 = c(0, 1, 1, 1, 1, 1, NA, NA),
+    x.bin2 = c(0, 1, 0, 1, 0.5, 1, NA, NA),
+    x.bin3 = c(0, 0, 0, 0.5, 0, 1, NA, NA),
+    y.bin1 = c(NA, NA, 0, 1, 1, 1, 1, 1),
+    y.bin2 = c(NA, NA, 0, 0, 15/35, 1, 1, 1),
+    y.bin3 = c(NA, NA, 0, 0, 0, 0, 5/35, 1)
+  )
+  expect_equal(train_out$data(cols = train_out$feature_names), dt_encoded)
+  expect_equal(predict_out$data(cols = predict_out$feature_names), dt_encoded)
+})
+
+test_that("PipeOpEncodePLTree - basic properties", {
+  skip_if_not_installed("rpart")
+
   task = mlr_tasks$get("mtcars")
-  expect_datapreproc_pipeop_class(PipeOpEncodePL, constargs = list(task_type = "TaskRegr"), task = task)
+  expect_datapreproc_pipeop_class(PipeOpEncodePLTree, constargs = list(task_type = "TaskRegr"), task = task)
 
   task = mlr_tasks$get("iris")
-  expect_datapreproc_pipeop_class(PipeOpEncodePL, task = task)
-  expect_datapreproc_pipeop_class(PipeOpEncodePL, constargs = list(task_type = "TaskClassif"), task = task)
+  expect_datapreproc_pipeop_class(PipeOpEncodePLTree, constargs = list(task_type = "TaskClassif"), task = task)
+})
+
+test_that("PipeOpEncodePLTree - TaskRegr train and predict", {
+  op = PipeOpEncodePLTree$new(task_type = "TaskRegr")
+  dt = data.table(
+    target = c(1.5, 20, 3.5, 30.8, 14.3, 90.4, 15, 60.4),
+    x = c(2, 8, 4, 10, 6, 12, NA, NA),
+    y = c(NA, NA, 10, 25, 40, 60, 65, 95)
+  )
+  task = TaskRegr$new(id = "test", backend = dt, target = "target")
+
+  train_out = op$train(list(task))[[1L]]
+  predict_out = op$predict(list(task))[[1L]]
+
+  # Test that bins are correct
+  #expect_equal(op$state$bins, list(x = c(), y = c()))
+
 })
 
-# Tests:
-# - different methods
-#    - with params (not all for regtree, hopefully)
-# - test on tasks with simple data that behaviour is as expected (compare dts)
-# - for different task types
-# - TODO: decide how to handle NAs in feature columns and test that
+test_that("PipeOpEncodePLTree - TaskClassif train and predict", {
+
+})