Random Foerst Model Fitting with R's Caret Package: A Complete Tutorial скачать в хорошем качестве

Random Foerst Model Fitting with R's Caret Package: A Complete Tutorial

Welcome to our comprehensive tutorial on model fitting using the R caret package. In this detailed guide, we cover every step necessary to understand and implement model fitting processes effectively. From loading essential libraries and preparing your data to advanced techniques like hyperparameter tuning and evaluating model performance, this tutorial ensures a deep understanding of the caret package's capabilities. What You Will Learn: Setting up your environment by loading required libraries. Preprocessing data and converting variables. Splitting data into training and testing sets to avoid overfitting. Implementing cross-validation techniques to ensure model robustness. Detailed explanation of hyperparameter tuning using the random forest model. Evaluating model performance using confusion matrices and understanding variable importance. Whether you're a student, researcher, or data science professional, this video equips you with the skills to perform sophisticated data analyses in R. Don't forget to engage with us by liking, subscribing, and commenting on your thoughts or questions below! link to download the data https://docs.google.com/spreadsheets/... code Comprehensive Steps for Model Fitting Using the Caret Package Step 1: Load Required Libraries Step 2: Read Data Step 3: Preprocess Data Step 4: Split Data into Training and Testing Sets Step 5: Set Up Cross-Validation Step 6: Define Hyperparameter Tuning Grid Step 7: Train the Model Step 8: Evaluate Model Performance Step 9: Variable Importance Actual code implementation Load Required Libraries library(caret) # for modeling library(readxl) # for reading Excel files library(randomForest) # for using the randomForest method Set working directory and Read Data setwd("E:\\Rworks\\model fitting using caret package") data = read_xlsx("Raisin_Dataset.xlsx") str(data) unique(data$Class) Ensure class variable is a factor data$Class = as.factor(data$Class) str(data) Splitting data into training and testing sets set.seed(546) # Ensure reproducibility training_indices =createDataPartition(data$Class, p=0.8, list=FALSE) is.matrix(training_indices) dim(training_indices) training_set = data[training_indices, ] testing_set = data[-training_indices, ] dim(testing_set) Setting up cross-validation and ensuring variable importance is recorded train_control = trainControl( method = "cv", number = 10, savePredictions = "final", classProbs = TRUE, # Store class probabilities #importance = TRUE, # Ensure variable importance is calculated summaryFunction = twoClassSummary # Use appropriate summary function for binary classification ) Defining hyperparameter tuning grid for the randomForest method tune_grid = expand.grid( mtry = c(2, 4, 6) # Number of variables considered at each split ) Training the model using randomForest model = train( Class ~ ., data = training_set, method = "rf", # Using randomForest for training trControl = train_control, tuneGrid = tune_grid ) model Extracting and plotting variable importance using caret's function importance = varImp(model, scale = TRUE) #barplot(importance$importance$Overall, names.arg = rownames(importance$importance)) plot(importance, main = "Variable Importance Plot") attributes(importance) Evaluate model performance print(model) confusionMatrix(predict(model, testing_set), testing_set$Class) summary(model$bestTune) reduced model Training the model using randomForest model_red = train( Class ~ Perimeter, data = training_set, method = "rf", # Using randomForest for training trControl = train_control, tuneGrid = tune_grid ) confusionMatrix(predict(model_red , testing_set), testing_set$Class) #DataScience, #RProgramming, #ModelFitting, #MachineLearning, #CaretPackage, #statisticalmodeling 00:00 - Introduction 01:30 - Loading Libraries 03:00 - Data Preprocessing 05:00 - Splitting Data 07:30 - Setting up Cross Validation 10:00 - Hyperparameter Tuning 12:30 - Training the Model 15:00 - Evaluating Model Performance 18:00 - Studying Variable Importance 20:00 - Conclusion & Additional Tips Facebook page:   / rajendrachoureisc   Mail Id: rajuchoure@gmail.com youtube playlist:    • R programming tutorials