View source for R Regression: penalized regression essentials ridge lasso elastic.net

 # Ref: http://www.sthda.com/english/articles/37-model-selection-essentials-in-r/153-penalized-regression-essentials-ridge-lasso-elastic-net/

 library(tidyverse)
 library(caret)
 library(glmnet)

 # Preparing the data
 # Load the data
 data("Boston", package = "MASS")
 # Split the data into training and test set
 set.seed(123)
 training.samples <- Boston$medv %>%
   createDataPartition(p = 0.8, list = FALSE)
 train.data  <- Boston[training.samples, ]
 test.data <- Boston[-training.samples, ]



 # Additional data preparation
 # Predictor variables
 x <- model.matrix(medv~., train.data)[,-1]
 # Outcome variable
 y <- train.data$medv



 # R functions
 glmnet(x, y, alpha = 1, lambda = NULL)





 # Computing ridge regression
 # Find the best lambda using cross-validation
 set.seed(123) 
 cv <- cv.glmnet(x, y, alpha = 0)
 # Display the best lambda value
 cv$lambda.min
 # Fit the final model on the training data
 model <- glmnet(x, y, alpha = 0, lambda = cv$lambda.min)
 # Display regression coefficients
 coef(model)
 # Make predictions on the test data
 x.test <- model.matrix(medv ~., test.data)[,-1]
 predictions <- model %>% predict(x.test) %>% as.vector()
 # Model performance metrics
 data.frame(
   RMSE = RMSE(predictions, test.data$medv),
   Rsquare = R2(predictions, test.data$medv)
 )







 # Computing lasso regression
 
 # Find the best lambda using cross-validation
 set.seed(123) 
 cv <- cv.glmnet(x, y, alpha = 1)
 # Display the best lambda value
 cv$lambda.min
 # Fit the final model on the training data
 model <- glmnet(x, y, alpha = 1, lambda = cv$lambda.min)
 # Dsiplay regression coefficients
 coef(model)
 # Make predictions on the test data
 x.test <- model.matrix(medv ~., test.data)[,-1]
 predictions <- model %>% predict(x.test) %>% as.vector()
 # Model performance metrics
 data.frame(
   RMSE = RMSE(predictions, test.data$medv),
   Rsquare = R2(predictions, test.data$medv)
 ) 








 # Computing elastic net regession
 
 # Build the model using the training set
 set.seed(123)
 model <- train(
   medv ~., data = train.data, method = "glmnet",
   trControl = trainControl("cv", number = 10),
   tuneLength = 10
 )
 # Best tuning parameter
 model$bestTune
 
 # Coefficient of the final model. You need
 # to specify the best lambda
 coef(model$finalModel, model$bestTune$lambda) 
 
 # Make predictions on the test data
 x.test <- model.matrix(medv ~., test.data)[,-1]
 predictions <- model %>% predict(x.test)
 # Model performance metrics
 data.frame(
   RMSE = RMSE(predictions, test.data$medv),
   Rsquare = R2(predictions, test.data$medv)
 )






 # Comparing the different models
 # Using caret package
 
 # Setup a grid range of lambda values:
 lambda <- 10^seq(-3, 3, length = 100)
 
 # Compute ridge regression:
 # Build the model
 set.seed(123)
 ridge <- train(
   medv ~., data = train.data, method = "glmnet",
   trControl = trainControl("cv", number = 10),
   tuneGrid = expand.grid(alpha = 0, lambda = lambda)
 )
 # Model coefficients
 coef(ridge$finalModel, ridge$bestTune$lambda)
 # Make predictions
 predictions <- ridge %>% predict(test.data)
 # Model prediction performance
 data.frame(
   RMSE = RMSE(predictions, test.data$medv),
   Rsquare = R2(predictions, test.data$medv)
 )

 # Compute lasso regression:
 # Build the model
 set.seed(123)
 lasso <- train(
   medv ~., data = train.data, method = "glmnet",
   trControl = trainControl("cv", number = 10),
   tuneGrid = expand.grid(alpha = 1, lambda = lambda)
 )
 # Model coefficients
 coef(lasso$finalModel, lasso$bestTune$lambda)
 # Make predictions
 predictions <- lasso %>% predict(test.data)
 # Model prediction performance
 data.frame(
   RMSE = RMSE(predictions, test.data$medv),
   Rsquare = R2(predictions, test.data$medv)
 )

 # Elastic net regression: 
 
 # Build the model
 set.seed(123)
 elastic <- train(
   medv ~., data = train.data, method = "glmnet",
   trControl = trainControl("cv", number = 10),
   tuneLength = 10
 )
 # Model coefficients
 coef(elastic$finalModel, elastic$bestTune$lambda)
 # Make predictions
 predictions <- elastic %>% predict(test.data)
 # Model prediction performance
 data.frame(
   RMSE = RMSE(predictions, test.data$medv),
   Rsquare = R2(predictions, test.data$medv)
 )

 # Comparing models performance:
 models <- list(ridge = ridge, lasso = lasso, elastic = elastic)
 resamples(models) %>% summary( metric = "RMSE")




==Referensi==
  
*  http://www.sthda.com/english/articles/37-model-selection-essentials-in-r/153-penalized-regression-essentials-ridge-lasso-elastic-net/

==Pranala Menarik==

* [[R]]