transfer from old repo

1 PA Decline/archive/prediction exercise.R

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+# https://www.machinelearningplus.com/machine-learning/caret-package/
+
+# install.packages(c('caret', 'skimr', 'RANN', 'randomForest', 'fastAdaboost', 'gbm', 'xgboost', 'caretEnsemble', 'C50', 'earth'))
+
+# Load the caret package
+library(caret)
+
+# Import dataset
+orange <- read.csv('https://raw.githubusercontent.com/selva86/datasets/master/orange_juice_withmissing.csv')
+
+# Structure of the dataframe
+str(orange)
+
+# See top 6 rows and 10 columns
+head(orange[, 1:10])
+
+# Create the training and test datasets
+set.seed(100)
+
+# Step 1: Get row numbers for the training data
+trainRowNumbers <- createDataPartition(orange$Purchase, p=0.8, list=FALSE)
+
+# Step 2: Create the training  dataset
+trainData <- orange[trainRowNumbers,]
+
+# Step 3: Create the test dataset
+testData <- orange[-trainRowNumbers,]
+
+# Store X and Y for later use.
+x = trainData[, 2:18]
+y = trainData$Purchase
+
+library(skimr)
+skimmed <- skim(trainData)
+skimmed
+
+# Create the knn imputation model on the training data
+preProcess_missingdata_model <- preProcess(trainData, method='knnImpute')
+preProcess_missingdata_model
+
+# Use the imputation model to predict the values of missing data points
+library(RANN)  # required for knnInpute
+trainData <- predict(preProcess_missingdata_model, newdata = trainData)
+anyNA(trainData)
+
+# One-Hot Encoding
+# Creating dummy variables is converting a categorical variable to as many binary variables as here are categories.
+dummies_model <- dummyVars(Purchase ~ ., data=trainData)
+
+# Create the dummy variables using predict. The Y variable (Purchase) will not be present in trainData_mat.
+trainData_mat <- predict(dummies_model, newdata = trainData)
+
+# # Convert to dataframe
+trainData <- data.frame(trainData_mat)
+
+# # See the structure of the new dataset
+str(trainData)
+
+
+preProcess_range_model <- preProcess(trainData, method='range')
+trainData <- predict(preProcess_range_model, newdata = trainData)
+
+# Append the Y variable
+trainData$Purchase <- y
+
+apply(trainData[, 1:10], 2, FUN=function(x){c('min'=min(x), 'max'=max(x))})
+
+
+featurePlot(x=trainData[,1:18],
+            y=factor(trainData$Purchase),
+            plot="box",
+            strip=strip.custom(par.strip.text=list(cex=.7)),
+            scales = list(x = list(relation="free"),
+                          y = list(relation="free")))
+
+featurePlot(x=trainData[,1:18],
+            y=factor(trainData$Purchase),
+            plot="density",
+            strip=strip.custom(par.strip.text=list(cex=.7)),
+            scales = list(x = list(relation="free"),
+                          y = list(relation="free")))
+
+# 5
+
+set.seed(100)
+options(warn=-1)
+
+subsets <- c(1:5, 10, 15, 18)
+
+ctrl <- rfeControl(functions = rfFuncs,
+                   method = "repeatedcv",
+                   repeats = 5,
+                   verbose = FALSE)
+
+lmProfile <- rfe(x=trainData[, 1:18], y=factor(trainData$Purchase),
+                 sizes = subsets,
+                 rfeControl = ctrl)
+
+lmProfile
+
+
+# See available algorithms in caret
+modelnames <- dput(names(getModelInfo()))
+# modelnames <- paste(names(getModelInfo()), collapse=',  ')
+modelnames
+
+
+# Set the seed for reproducibility
+set.seed(100)
+
+# Train the model using randomForest and predict on the training data itself.
+model_mars = train(Purchase ~ ., data=trainData, method='earth')
+fitted <- predict(model_mars)
+
+model_mars
+
+
+plot(model_mars, main="Model Accuracies with MARS")
+
+varimp_mars <- varImp(model_mars)
+plot(varimp_mars, main="Variable Importance with MARS")
+
+
+## 6.4
+
+# Step 1: Impute missing values 
+testData2 <- predict(preProcess_missingdata_model, testData)  
+
+# Step 2: Create one-hot encodings (dummy variables)
+testData3 <- predict(dummies_model, testData2)
+
+# Step 3: Transform the features to range between 0 and 1
+testData4 <- predict(preProcess_range_model, testData3)
+
+# View
+head(testData4[, 1:10])
+
+predicted <- predict(model_mars, testData4)
+head(predicted)
+
+# Compute the confusion matrix
+confusionMatrix(reference = factor(testData$Purchase), data = predicted, mode='everything', positive='MM')