diff --git a/R/SklearnClassifierSettings.R b/R/SklearnClassifierSettings.R
index 61193ab1..8f29ba3c 100644
--- a/R/SklearnClassifierSettings.R
+++ b/R/SklearnClassifierSettings.R
@@ -98,7 +98,7 @@ setAdaBoost <- function(nEstimators = list(10, 50, 200),
 }
 
 
-adaBoostClassifierInputs <- function(classifier, param) {
+AdaBoostClassifierInputs <- function(classifier, param) {
   model <- classifier(
     n_estimators = param[[which.max(names(param) == "nEstimators")]],
     learning_rate = param[[which.max(names(param) == "learningRate")]],
@@ -330,7 +330,7 @@ setDecisionTree <- function(criterion = list("gini"),
 }
 
 
-decisionTreeClassifierInputs <- function(classifier, param) {
+DecisionTreeClassifierInputs <- function(classifier, param) {
   model <- classifier(
     criterion = param[[which.max(names(param) == "criterion")]],
     splitter = param[[which.max(names(param) == "splitter")]],
@@ -568,7 +568,7 @@ setNaiveBayes <- function() {
   return(result)
 }
 
-gaussianNBInputs <- function(classifier, param) {
+GaussianNBInputs <- function(classifier, param) {
   model <- classifier()
 
   return(model)
@@ -727,7 +727,7 @@ setRandomForest <- function(ntrees = list(100, 500),
 }
 
 
-randomForestClassifierInputs <- function(classifier, param) {
+RandomForestClassifierInputs <- function(classifier, param) {
   model <- classifier(
     n_estimators = param[[which.max(names(param) == "ntrees")]],
     criterion = param[[which.max(names(param) == "criterion")]],
diff --git a/man/createFeatureEngineeringSettings.Rd b/man/createFeatureEngineeringSettings.Rd
index 0b7a0a8d..9772a9a1 100644
--- a/man/createFeatureEngineeringSettings.Rd
+++ b/man/createFeatureEngineeringSettings.Rd
@@ -8,7 +8,7 @@ createFeatureEngineeringSettings(type = "none")
 }
 \arguments{
 \item{type}{(character) Choice of:  \itemize{
-\item'none' No feature engineering - this is the default 
+\item'none' No feature engineering - this is the default
 }}
 }
 \value{
diff --git a/man/setAdaBoost.Rd b/man/setAdaBoost.Rd
index adcc4f6f..a13762b2 100644
--- a/man/setAdaBoost.Rd
+++ b/man/setAdaBoost.Rd
@@ -7,7 +7,7 @@
 setAdaBoost(
   nEstimators = list(10, 50, 200),
   learningRate = list(1, 0.5, 0.1),
-  algorithm = list("SAMME.R"),
+  algorithm = list("SAMME"),
   seed = sample(1000000, 1)
 )
 }
@@ -17,7 +17,7 @@ setAdaBoost(
 \item{learningRate}{(list) Weight applied to each classifier at each boosting iteration. A higher learning rate increases the contribution of each classifier. There is a trade-off between the learningRate and nEstimators parameters
 There is a trade-off between learningRate and nEstimators.}
 
-\item{algorithm}{(list) If ‘SAMME.R’ then use the SAMME.R real boosting algorithm. base_estimator must support calculation of class probabilities. If ‘SAMME’ then use the SAMME discrete boosting algorithm. The SAMME.R algorithm typically converges faster than SAMME, achieving a lower test error with fewer boosting iterations.}
+\item{algorithm}{Only ‘SAMME’ can be provided. The 'algorithm' argument will be deprecated in scikit-learn 1.8.}
 
 \item{seed}{A seed for the model}
 }
@@ -26,8 +26,9 @@ Create setting for AdaBoost with python DecisionTreeClassifier base estimator
 }
 \examples{
 \dontrun{
-model.adaBoost <- setAdaBoost(nEstimators = list(10,50,200), learningRate = list(1, 0.5, 0.1),
-                              algorithm = list('SAMME.R'), seed = sample(1000000,1)
-                              )
+model.adaBoost <- setAdaBoost(
+  nEstimators = list(10, 50, 200), learningRate = list(1, 0.5, 0.1),
+  algorithm = list("SAMME.R"), seed = sample(1000000, 1)
+)
 }
 }
diff --git a/man/setDecisionTree.Rd b/man/setDecisionTree.Rd
index 96f56e74..b5633772 100644
--- a/man/setDecisionTree.Rd
+++ b/man/setDecisionTree.Rd
@@ -2,7 +2,7 @@
 % Please edit documentation in R/SklearnClassifierSettings.R
 \name{setDecisionTree}
 \alias{setDecisionTree}
-\title{Create setting for the scikit-learn 1.0.1 DecisionTree with python}
+\title{Create setting for the scikit-learn DecisionTree with python}
 \usage{
 setDecisionTree(
   criterion = list("gini"),
@@ -42,10 +42,10 @@ setDecisionTree(
 \item{seed}{The random state seed}
 }
 \description{
-Create setting for the scikit-learn 1.0.1 DecisionTree with python
+Create setting for the scikit-learn DecisionTree with python
 }
 \examples{
 \dontrun{
-model.decisionTree <- setDecisionTree(maxDepth=10,minSamplesLeaf=10, seed=NULL )
+model.decisionTree <- setDecisionTree(maxDepth = 10, minSamplesLeaf = 10, seed = NULL)
 }
 }
diff --git a/man/setRandomForest.Rd b/man/setRandomForest.Rd
index dbe7ad0f..7812e522 100644
--- a/man/setRandomForest.Rd
+++ b/man/setRandomForest.Rd
@@ -65,7 +65,9 @@ Create setting for random forest model with python (very fast)
 }
 \examples{
 \dontrun{
-model.rf <- setRandomForest(mtries=list('auto',5,20),  ntrees=c(10,100),
-                           maxDepth=c(5,20))
+model.rf <- setRandomForest(
+  mtries = list("auto", 5, 20), ntrees = c(10, 100),
+  maxDepth = c(5, 20)
+)
 }
 }
diff --git a/man/setSVM.Rd b/man/setSVM.Rd
index f21a202e..0b7af491 100644
--- a/man/setSVM.Rd
+++ b/man/setSVM.Rd
@@ -43,6 +43,6 @@ Create setting for the python sklearn SVM (SVC function)
 }
 \examples{
 \dontrun{
-model.svm <- setSVM(kernel='rbf', seed = NULL)
+model.svm <- setSVM(kernel = "rbf", seed = NULL)
 }
 }
diff --git a/tests/testthat/test-sklearnClassifierSettings.R b/tests/testthat/test-sklearnClassifierSettings.R
index f71d79dd..ab56a389 100644
--- a/tests/testthat/test-sklearnClassifierSettings.R
+++ b/tests/testthat/test-sklearnClassifierSettings.R
@@ -20,7 +20,7 @@ test_that("setAdaBoost settings work checks", {
   expect_equal(attr(adset$param, "settings")$pythonClass, "AdaBoostClassifier")
 
 
-  inputs <- adaBoostClassifierInputs(list, adset$param[[1]])
+  inputs <- AdaBoostClassifierInputs(list, adset$param[[1]])
   expect_equal(
     names(inputs),
     c("n_estimators", "learning_rate", "algorithm", "random_state")
@@ -101,7 +101,7 @@ test_that("setNaiveBayes settings work checks", {
   expect_equal(attr(nbset$param, "settings")$pythonModule, "sklearn.naive_bayes")
   expect_equal(attr(nbset$param, "settings")$pythonClass, "GaussianNB")
 
-  inputs <- gaussianNBInputs(list, nbset$param[[1]])
+  inputs <- GaussianNBInputs(list, nbset$param[[1]])
   expect_equal(names(inputs), NULL)
 })
 
@@ -137,7 +137,7 @@ test_that("setRandomForest settings work checks", {
   expect_equal(attr(rfset$param, "settings")$pythonModule, "sklearn.ensemble")
   expect_equal(attr(rfset$param, "settings")$pythonClass, "RandomForestClassifier")
 
-  inputs <- randomForestClassifierInputs(list, rfset$param[[1]])
+  inputs <- RandomForestClassifierInputs(list, rfset$param[[1]])
   expect_equal(
     names(inputs),
     c(