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Lasso Regression in R Programming
  • Last Updated : 28 Jul, 2020

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.
lasso regression

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.

Implementation in R

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).

# Loading data
train = fread("Train_UWu5bXk.csv")
test = fread("Test_u94Q5KV.csv")
# Structure 


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.

# Installing Packages
# load packages
library(data.table) # used for reading and manipulation of data
library(dplyr)      # used for data manipulation and joining
library(glmnet)     # used for regression
library(ggplot2)    # used for ploting 
library(caret)      # used for modeling
library(xgboost)    # used for building XGBoost model
library(e1071)      # used for skewness
library(cowplot)    # used for combining multiple plots 
# Loding datasets
train = fread("Train_UWu5bXk.csv")
test = fread("Test_u94Q5KV.csv")
# Setting test dataset
# Combining datasets
# add Item_Outlet_Sales to test data
test[, Item_Outlet_Sales := NA
combi = rbind(train, test)
# Missing Value Treatment
missing_index = which($Item_Weight))
for(i in missing_index)
  item = combi$Item_Identifier[i]
  combi$Item_Weight[i] = 
      mean(combi$Item_Weight[combi$Item_Identifier == item], 
                                                 na.rm = T)
# Replacing 0 in Item_Visibility with mean
zero_index = which(combi$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 numerical
combi[, 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 numerical
ohe_1 = dummyVars("~.", data = combi[, -c("Item_Identifier"
                                          "Item_Type")], fullRank = T)
ohe_df = data.table(predict(ohe_1, combi[, -c("Item_Identifier"
combi = cbind(combi[, "Item_Identifier"], ohe_df)
# Remove skewness
# log + 1 to avoid division by zero
combi[, Item_Visibility := log(Item_Visibility + 1)] 
# Scaling and Centering data
num_vars = which(sapply(combi, is.numeric)) # index of numeric features
num_vars_names = names(num_vars)
combi_numeric = combi[, setdiff(num_vars_names, 
                               with = F]
prep_num = preProcess(combi_numeric, 
                      method=c("center", "scale"))
combi_numeric_norm = predict(prep_num, combi_numeric)
# removing numeric independent variables
combi[, setdiff(num_vars_names, 
                "Item_Outlet_Sales") := NULL]
combi = cbind(combi, combi_numeric_norm)
# splitting data back to train and test
train = combi[1:nrow(train)]
test = combi[(nrow(train) + 1):nrow(combi)]
# Removing Item_Outlet_Sales
test[, Item_Outlet_Sales := NULL
# Model Building :Lasso Regression
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 model
lasso_model = train(x = train[, -c("Item_Identifier"
                    y = train$Item_Outlet_Sales,
                    method = "glmnet",
                    trControl = control,
                    tuneGrid = Grid_reg
# mean validation score
# Plot
plot(lasso_model, main = "Lasso Regression")


  • 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:
    output plot

    The regularization parameter increases, RMSE remains constant.

    So, Lasso regression finds its applications in many sectors of industry and used with full capacity.

    My Personal Notes arrow_drop_up
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