Lasso Regression in R Programming

28 Sep, 2021
Lasso regression is a classification algorithm that uses shrinkage in simple and sparse models(i.e model with fewer parameters). In Shrinkage, data values are shrunk towards a central point like the mean. Lasso regression is a regularized regression algorithm that performs L1 regularization which adds penalty equal to the absolute value of the magnitude of coefficients.
“LASSO” stands for Least Absolute Shrinkage and Selection Operator. Lasso regression is good for models showing high levels of multicollinearity or when you want to automate certain parts of model selection i.e variable selection or parameter elimination. Lasso regression solutions are quadratic programming problems that can best solve with software like RStudio, Matlab, etc. It has the ability to select predictors. 
The algorithm minimizes the sum of squares with constraint. Some Beta are shrunk to zero that results in a regression model. A tuning parameter lambda controls the strength of the L1 regularization penalty. lambda is basically the amount of shrinkage: 
When lambda = 0, no parameters are eliminated. 
As lambda increases, more and more coefficients are set to zero and eliminated & bias increases. 
When lambda = infinity, all coefficients are eliminated. 
As lambda decreases, variance increases. 
Also, If an intercept is included in the model, it is left unchanged. Now let’s implementing Lasso regression in R programming.
The Dataset
Big Mart dataset consists of 1559 products across 10 stores in different cities. Certain attributes of each product and store have been defined. It consists of 12 features i.e Item_Identifier( is a unique product ID assigned to every distinct item), Item_Weight(includes the weight of the product), Item_Fat_Content(describes whether the product is low fat or not), Item_Visibility(mentions the percentage of the total display area of all products in a store allocated to the particular product), Item_Type(describes the food category to which the item belongs), Item_MRP(Maximum Retail Price (list price) of the product), Outlet_Identifier(unique store ID assigned. It consists of an alphanumeric string of length 6), Outlet_Establishment_Year(mentions the year in which store was established), Outlet_Size(tells the size of the store in terms of ground area covered), Outlet_Location_Type(tells about the size of the city in which the store is located), Outlet_Type(tells whether the outlet is just a grocery store or some sort of supermarket) and Item_Outlet_Sales( sales of the product in the particular store).
R
# Loading datatrain = fread("Train_UWu5bXk.csv")test = fread("Test_u94Q5KV.csv")   # Structurestr(train)
Output: 
Performing Lasso Regression on Dataset
Using the Lasso regression algorithm on the dataset which includes 12 features with 1559 products across 10 stores in different cities.
R
# Installing Packagesinstall.packages("data.table")install.packages("dplyr")install.packages("glmnet")install.packages("ggplot2")install.packages("caret")install.packages("xgboost")install.packages("e1071")install.packages("cowplot") # load packageslibrary(data.table) # used for reading and manipulation of datalibrary(dplyr)      # used for data manipulation and joininglibrary(glmnet)     # used for regressionlibrary(ggplot2)    # used for plotinglibrary(caret)      # used for modelinglibrary(xgboost)    # used for building XGBoost modellibrary(e1071)      # used for skewnesslibrary(cowplot)    # used for combining multiple plots # Loading datasetstrain = fread("Train_UWu5bXk.csv")test = fread("Test_u94Q5KV.csv") # Setting test dataset# Combining datasets# add Item_Outlet_Sales to test datatest[, Item_Outlet_Sales := NA] combi = rbind(train, test) # Missing Value Treatmentmissing_index = which(is.na(combi$Item_Weight))for(i in missing_index){  item = combi$Item_Identifier[i]  combi$Item_Weight[i] =      mean(combi$Item_Weight[combi$Item_Identifier == item],                                                 na.rm = T)} # Replacing 0 in Item_Visibility with meanzero_index = which(combi$Item_Visibility == 0)for(i in zero_index){  item = combi$Item_Identifier[i]  combi$Item_Visibility[i] =      mean(combi$Item_Visibility[combi$Item_Identifier == item],                                                     na.rm = T)} # Label Encoding# To convert categorical in numericalcombi[, Outlet_Size_num := ifelse(Outlet_Size == "Small", 0,                                 ifelse(Outlet_Size == "Medium",                                                        1, 2))] combi[, Outlet_Location_Type_num :=         ifelse(Outlet_Location_Type == "Tier 3", 0,                ifelse(Outlet_Location_Type == "Tier 2", 1, 2))] combi[, c("Outlet_Size", "Outlet_Location_Type") := NULL] # One Hot Encoding# To convert categorical in numericalohe_1 = dummyVars("~.", data = combi[, -c("Item_Identifier",                                          "Outlet_Establishment_Year",                                          "Item_Type")], fullRank = T)ohe_df = data.table(predict(ohe_1, combi[, -c("Item_Identifier",                                           "Outlet_Establishment_Year",                                           "Item_Type")])) combi = cbind(combi[, "Item_Identifier"], ohe_df) # Remove skewnessskewness(combi$Item_Visibility)skewness(combi$price_per_unit_wt) # log + 1 to avoid division by zerocombi[, Item_Visibility := log(Item_Visibility + 1)] # Scaling and Centering datanum_vars = which(sapply(combi, is.numeric)) # index of numeric featuresnum_vars_names = names(num_vars) combi_numeric = combi[, setdiff(num_vars_names,                              "Item_Outlet_Sales"),                               with = F] prep_num = preProcess(combi_numeric,                      method=c("center", "scale"))combi_numeric_norm = predict(prep_num, combi_numeric) # removing numeric independent variablescombi[, setdiff(num_vars_names,                "Item_Outlet_Sales") := NULL]combi = cbind(combi, combi_numeric_norm) # splitting data back to train and testtrain = combi[1:nrow(train)]test = combi[(nrow(train) + 1):nrow(combi)] # Removing Item_Outlet_Salestest[, Item_Outlet_Sales := NULL] # Model Building :Lasso Regressionset.seed(123)control = trainControl(method ="cv", number = 5)Grid_la_reg = expand.grid(alpha = 1,              lambda = seq(0.001, 0.1, by = 0.0002)) # Training lasso regression modellasso_model = train(x = train[, -c("Item_Identifier",                               "Item_Outlet_Sales")],                    y = train$Item_Outlet_Sales,                    method = "glmnet",                    trControl = control,                    tuneGrid = Grid_reg                    )lasso_model # mean validation scoremean(lasso_model$resample$RMSE) # Plotplot(lasso_model, main = "Lasso Regression")
Output: 
Model lasso_model: 
The Lasso regression model uses the alpha value as 1 and lambda value as 0.1. RMSE was used to select the optimal model using the smallest value.
Mean validation score: 
The mean validation score of the model is 1128.869. 
Plot: 
The regularization parameter increases, RMSE remains constant.
sumitgumber28
R Data-science
R Machine-Learning
R Language
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