Mаth instructiоns аre used tо cоmpаre numerical data stored by the PLC
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Questiоn 4: Regulаrized vаriаble selectiоn (12 tоtal points) 4a)(6 points) You will be performing Ridge regression on the dataset "trainData". Answer the following questions: i) Use cv.glmnet() to find the lambda value that minimizes the cross-validation error using 10 fold CV and state the value of the optimal lambda. ii) Fit the model with 100 values for lambda. iii) Extract the coefficients from ii) using the optimal lambda from i), list the coefficients that are selected. iv) Plot the coefficient path and place the optimal lambda from i) on the plot. Analyze the plot and comment on which coefficients are shrunk to zero. 4b)(6 points) You will be performing group lasso using the dataset trainData. Create the following groups below for your model Note: Only these 5 variables/3 groups should be used in your group lasso model. Group1: Age Group2: ExperienceYears and RemoteWorkHoursPerWeek Group3: JobSatisfaction and TechToolsUsed Answer the following questions: Find the lambda value that minimizes the cross-validation error using 10 fold CV and state the value of the optimal lambda. Fit the model with 100 values for lambda. Extract the coefficients from ii) using the optimal lambda from i), display the coefficients of the variables and comment on the magnitude and direction of the variables on the response. Plot the coefficient path and place the optimal lambda from i) on the plot and then analyze/interpret the plot