Chapter 6 Part 1

Subset Selection

Author

Affiliation

Tyler George

Cornell College
STA 362 Spring 2024 Block 8

Setup

library(tidyverse)
library(tidymodels)
library(gridExtra)
library(ISLR2)
library(leaps)

Recall

Linear model \[Y = \beta_0 + \beta_1X_1+...\beta_pX_p+\epsilon \text{ where }\epsilon \sim N(0,\sigma_{\epsilon})\]
Despite its simplicity, the linear model has distinct advantages in terms of its interpretability and often shows good predictive performance.

How can we improve it?

Why

Prediction accuracy when $p>n$, to control variance
Removing irrelevant features, we can get a simpler model to interpret
Automating this procedure, by doing feature selection

Three classes of models

Subset Selection (today)
Shrinkage
Dimension Reduction

Today example: Credit

data("Credit")
set.seed(343543)
c_split <- initial_split(Credit,.75)
c_train <- training(c_split)
c_test <- testing(c_split)

Subset Selection

Identify a subset of the $p$ predictors that we believe are related to the response
Generally going to use least squares, with more or less predictors and compare

Best Subset Selection

Start with a null, constant model. Uses mean to predict for all values of response. Call it $\mathcal{M}_0$
For each $k=1,2,...,p$:

Fit all $\binom{p}{k}$ models that contain exactly $k$ predictors
Pick the best model among them, best according to smallest RSS or largest $R^2$. Call it $\mathcal{M}_k$

Select a single best model among $\mathcal{M}_0, \mathcal{M}_1,...,\mathcal{M}_p$ using cross-validation prediction error, $C_p$, AIC, BIC, or adjusted $R^2$

Example - Credit Data

RSS and $R^2$ are displayed.
Red frontier tracks the best model for a given number of predictors, according to RSS and $R^2$.
Data set contains only ten predictors, the x-axis ranges from 1 to 11, since one of the variables is categorical and takes on three values, leading to the creation of two dummy variables

Extensions

These approaches can be applied to other types of regression such as logistic
In other models, you would use deviance instead of RSS (lower is still better)

Best Subsets - not tidymodels

Predict balance based on Income, Limit, Rating, Cards, Age, Education, Own, Married, and Region
Not in tidymodels
(*) indicates the given variable is in the corresponding model

Code

best_ss_model <- regsubsets(Balance ~.,data = c_train )

summary(best_ss_model)

Subset selection object
Call: regsubsets.formula(Balance ~ ., data = c_train)
11 Variables  (and intercept)
            Forced in Forced out
Income          FALSE      FALSE
Limit           FALSE      FALSE
Rating          FALSE      FALSE
Cards           FALSE      FALSE
Age             FALSE      FALSE
Education       FALSE      FALSE
OwnYes          FALSE      FALSE
StudentYes      FALSE      FALSE
MarriedYes      FALSE      FALSE
RegionSouth     FALSE      FALSE
RegionWest      FALSE      FALSE
1 subsets of each size up to 8
Selection Algorithm: exhaustive
         Income Limit Rating Cards Age Education OwnYes StudentYes MarriedYes RegionSouth
1  ( 1 ) " "    " "   "*"    " "   " " " "       " "    " "        " "        " "        
2  ( 1 ) "*"    " "   "*"    " "   " " " "       " "    " "        " "        " "        
3  ( 1 ) "*"    "*"   " "    " "   " " " "       " "    "*"        " "        " "        
4  ( 1 ) "*"    "*"   " "    "*"   " " " "       " "    "*"        " "        " "        
5  ( 1 ) "*"    "*"   " "    "*"   "*" " "       " "    "*"        " "        " "        
6  ( 1 ) "*"    "*"   "*"    "*"   "*" " "       " "    "*"        " "        " "        
7  ( 1 ) "*"    "*"   "*"    "*"   "*" " "       " "    "*"        "*"        " "        
8  ( 1 ) "*"    "*"   "*"    "*"   "*" "*"       " "    "*"        "*"        " "        
         RegionWest
1  ( 1 ) " "       
2  ( 1 ) " "       
3  ( 1 ) " "       
4  ( 1 ) " "       
5  ( 1 ) " "       
6  ( 1 ) " "       
7  ( 1 ) " "       
8  ( 1 ) " "

Best Subsets

Save the summary and see whats included

Code

reg_summary <- summary(best_ss_model)
names(reg_summary) # See what metrics are included

[1] "which"  "rsq"    "rss"    "adjr2"  "cp"     "bic"    "outmat" "obj"

We can access each of these with $
This gives you the value for each of the best models with different number of predictors

Code

reg_summary$rsq

[1] 0.7419596 0.8645677 0.9470542 0.9514489 0.9521160 0.9525191 0.9526977 0.9527695

Best Subsets - function

Let’s make the plots we saw before to help us choose.

Code

create_metrics_table <- function(X){
  K <- length(X$rsq)
  metrics_df <- data.frame(num_pred= 1:K, # K different models
                           Rsq = X$rsq,
                           rss = X$rss,
                           adjr2 = X$adjr2,
                           cp = X$cp,
                           bic = X$bic) |>
    pivot_longer(cols=Rsq:bic,
                 names_to = "metric",values_to = "metric_val")
    # This pivot puts the metric values in 1 column 
        # and creates another column for the name of 
        # the metric
   return(metrics_df)
}

Best Subsets - Graph

Code

metrics_df <- create_metrics_table(reg_summary)

metrics_df |> 
  ggplot(aes(y=metric_val,x=num_pred))+
    geom_line() + geom_point()+
    facet_wrap(~metric,scales = "free_y")

Best Subsets - Best Values

We can use the which function to tell us the vector element that has a max or min

Code

which.max(reg_summary$rsq)

[1] 8

Best Subsets - Looking at model

We can use the coef function to look at the coefficients of a model

Code

coef(best_ss_model,8)

 (Intercept)       Income        Limit       Rating        Cards          Age 
-442.9663782   -7.6554801    0.2039346    0.8930414   17.5046106   -0.7496664 
   Education   StudentYes   MarriedYes 
  -1.2622327  425.4323902  -12.4991226

Once we choose the best model, we can refit it with tidymodels!

`Application Exercise`

Try best subset regression on the penguins data in the palmerpenguins package to find the best model to predict weight.

Stepwise Selection

Why is Best Subsets not ideal?

Best subsets does not work well when $p$ is large as it increases the chance we $overfit$ on training data.
There is large search space for models!
High variance of coefficient estimates
stepwise methods, are far more restrictive and more attractable

Forward Stepwise Selection

Begins with a model containing no predictors, and then adds predictors to the model, one-at-a-time, until all of the predictors are in the model.
At each step the variable that gives the greatest additional improvement to the fit is added to the model.

Forward Stepwise Selection

Start with a null, constant model. Uses mean to predict for all values of response. Call it $\mathcal{M}_0$
For each $k=0,1,2,...,p-1$:

Fit all $p-k$ models that augment the predictors in $\mathcal{M}_k$ with one additional predictor.
Chose the best model among the $p-k$ models and call it $\mathcal{M}_{k+1}$, best according to smallest RSS or largest $R^2$.

Select a single best model among $\mathcal{M}_0, \mathcal{M}_1,...,\mathcal{M}_p$ using cross-validation prediction error, $C_p$, AIC, BIC, or adjusted $R^2$

Forward Stepwise Selection

Computational advantage over best subset
Not guaranteed to find the best model of all $2^p$ subsets of predictors.

Why not?

Forward Stepwise Credit Example

Num Variables	Best Subset	Forward Stepwise
One	`rating`	`rating`
Two	`rating`, `income`	`rating`, `income`
Three	`rating`, `income`, `student`	`rating`, `income`, `student`
Four	`student`, `limit`	`student`, `limit`

The first four selected models for best subset selection and forward stepwise selection on the Credit data set.

The first three models are identical but the fourth models differ.

Backward Stepwise Selection

Like forward stepwise selection, backward stepwise selection provides an efficient alternative to best subset selection.
Unlike forward stepwise selection, it begins with the full least squares model containing all p predictors, and then iteratively removes the least useful predictor, one-at-a-time.

Backward Stepwise Selection

Let $\mathcal{M}_p$ denote the full model, which contains $p$ predictors
For each $k=p,p-1,...,1$:

Fit all $k$ models that contain all but one of the predictors in $\mathcal{M}_k$, for a total of $k-1$ predictors
Chose the best model among the $k$ models and call it $\mathcal{M}_{k-1}$, best according to smallest RSS or largest $R^2$.

Select a single best model among $\mathcal{M}_0, \mathcal{M}_1,...,\mathcal{M}_p$ using cross-validation prediction error, $C_p$, AIC, BIC, or adjusted $R^2$

Backward Stepwise Selection

Like forward stepwise selection, the backward selection approach searches through only $1+ p(p + 1)=2$ models, and so can be applied in settings where $p$ is too large to apply best subset selection
Like forward stepwise selection, backward stepwise selection is not guaranteed to yield the best model containing a subset of the $p$ predictors.
Backward selection requires that the number of samples $n$ is larger than the number of variables $p$ (so that the full model can be fit). In contrast, forward stepwise can be used even when $n < p$, and so is the only viable subset method when $p$ is very large.

Choosing an Optimal Model

The model containing all of the predictors will always have the smallest RSS and the largest $R^2$, since these quantities are related to the training error.
We wish to choose a model with low test error, not a model with low training error. Recall that training error is usually a poor estimate of test error.
Thus RSS and $R^2$ are not suitable for selecting the best model among a collection of models with different numbers of predictors.

Estimating Test error

We can indirectly estimate test error by making an adjustment to the training error to account for the bias due to overfitting.
We can directly estimate the test error, using either a validation set approach or a cross-validation approach, as discussed in previous lectures.

$C_p$, AIC, BIC, Adjusted $R^2$

These adjust the training error for the model size, and can be used to select among a set of models with different numbers of variables.

$C_p$, AIC, BIC, Adjusted $R^2$

This displays $C_p$, BIC, and adjusted $R^2$ for the best model of each size produced by best subset selection on the Credit data set.

Mallow’s $C_p$

\[C_p = \frac{1}{n}(RSS + 2d\hat{\sigma}^2)\]

where $d$ is the total number of parameters used and $\hat{\sigma}^2$ is an estimate of the variance of the error $\epsilon$ associated with each response measurement.

Akaike information criterion (AIC)

Defined for large class of models fit by maximum likelihood:
\[AIC = -2log(L)+2\cdot d\] where $L$ is the maximized value of the likelihood function for the estimated model.
In the case linear models with normal errors, maximum likelihood and least squares are the same thing, and $C_p$ and AIC are equal.

Bayesian information criterion (BIC)

\[BIC = \frac{1}{n}(RSS + log(n)d\hat{\sigma}^2)\]
Like $C_p$, the BIC will tend to take on a small value for a model with low test error, and so generally we select the model that has the lowest BIC value
Note that BIC replaces the $2d\hat{\sigma}^2$ used by $C_p$ with a $log(n)d\hat{\sigma}^2$ term, where $n$ is the number of observations
Since $log(n)>2$ for any $n>7$, the BIC statistics places a heavier penalty on models with many variance and hence results in the selection of smaller models than $C_p$

Adjusted $R^2$

For least squares model with $d$ variables, the adjusted $R^2$ is
\[\text{Adjusted } R^2 = 1-\frac{RSS/(n-d-1)}{TSS/(n-1)}\]
Unlike $C_p$, AIC, and BIC, for which a small value indicates a model with a low test error, a large value of adjusted $R^2$ indicates a model with a small test error.
Maximizing the adjusted $R^2$ is equivalent to minimizing $\frac{RSS}{n-d-1}$. While RSS always decreases as the number of variables in the model increases, $\frac{RSS}{n-d-1}$ may increase or decrease, due to the presence of $d$ in the denominator.
Unlike the $R^2$ statistic, the adjusted $R^2$ statistic pays a price for the inclusion of unnecessary variables in the model.

Validation and CV Sets

Each of the procedures returns a sequence of models $\mathcal{M}_k$ indexed by model size $k = 0, 1, 2,,,$. We want to select $\hat{k}$. Once selected, we will return model $\mathcal{M}_{\hat{k}}$
We compute the validation set error or the cross-validation error for each model $\mathcal{M}_k$ under consideration, and then select the $k$ for which the resulting estimated test error is smallest.
This procedure has an advantage relative to AIC, BIC, $C_p$, and adjusted R2, in that it provides a direct estimate of the test error, and doesn’t require an estimate of the error variance $\sigma^2$.
It can also be used in a wider range of model selection tasks, even in cases where it is hard to pinpoint the model degrees of freedom (e.g. the number of predictors in the model) or hard to estimate the error variance $\sigma^2$

The validation errors were calculated by randomly selecting three-quarters of the observations as the training set, and the remainder as the validation set.
The cross-validation errors were computed using $k = 10 folds$. In this case, the validation and cross-validation methods both result in a six-variable model.
However, all three approaches suggest that the four-, five-, and six-variable models are roughly equivalent in terms of their test errors.
In this setting, we can select a model using the one-standard-error rule. We first calculate the standard error of the estimated test RSS for each model size, and then select the smallest model for which the estimated test error is within one standard error of the lowest point on the curve.

Forward Stepwise in R

fsw_model <- regsubsets(Balance ~.,data = c_train,method="forward")

summary(fsw_model)

Subset selection object
Call: regsubsets.formula(Balance ~ ., data = c_train, method = "forward")
11 Variables  (and intercept)
            Forced in Forced out
Income          FALSE      FALSE
Limit           FALSE      FALSE
Rating          FALSE      FALSE
Cards           FALSE      FALSE
Age             FALSE      FALSE
Education       FALSE      FALSE
OwnYes          FALSE      FALSE
StudentYes      FALSE      FALSE
MarriedYes      FALSE      FALSE
RegionSouth     FALSE      FALSE
RegionWest      FALSE      FALSE
1 subsets of each size up to 8
Selection Algorithm: forward
         Income Limit Rating Cards Age Education OwnYes StudentYes MarriedYes RegionSouth
1  ( 1 ) " "    " "   "*"    " "   " " " "       " "    " "        " "        " "        
2  ( 1 ) "*"    " "   "*"    " "   " " " "       " "    " "        " "        " "        
3  ( 1 ) "*"    " "   "*"    " "   " " " "       " "    "*"        " "        " "        
4  ( 1 ) "*"    "*"   "*"    " "   " " " "       " "    "*"        " "        " "        
5  ( 1 ) "*"    "*"   "*"    "*"   " " " "       " "    "*"        " "        " "        
6  ( 1 ) "*"    "*"   "*"    "*"   "*" " "       " "    "*"        " "        " "        
7  ( 1 ) "*"    "*"   "*"    "*"   "*" " "       " "    "*"        "*"        " "        
8  ( 1 ) "*"    "*"   "*"    "*"   "*" "*"       " "    "*"        "*"        " "        
         RegionWest
1  ( 1 ) " "       
2  ( 1 ) " "       
3  ( 1 ) " "       
4  ( 1 ) " "       
5  ( 1 ) " "       
6  ( 1 ) " "       
7  ( 1 ) " "       
8  ( 1 ) " "

Backward Stepwise in R

bsw_model <- regsubsets(Balance ~.,data = c_train,method="backward")

summary(bsw_model)

Subset selection object
Call: regsubsets.formula(Balance ~ ., data = c_train, method = "backward")
11 Variables  (and intercept)
            Forced in Forced out
Income          FALSE      FALSE
Limit           FALSE      FALSE
Rating          FALSE      FALSE
Cards           FALSE      FALSE
Age             FALSE      FALSE
Education       FALSE      FALSE
OwnYes          FALSE      FALSE
StudentYes      FALSE      FALSE
MarriedYes      FALSE      FALSE
RegionSouth     FALSE      FALSE
RegionWest      FALSE      FALSE
1 subsets of each size up to 8
Selection Algorithm: backward
         Income Limit Rating Cards Age Education OwnYes StudentYes MarriedYes RegionSouth
1  ( 1 ) " "    "*"   " "    " "   " " " "       " "    " "        " "        " "        
2  ( 1 ) "*"    "*"   " "    " "   " " " "       " "    " "        " "        " "        
3  ( 1 ) "*"    "*"   " "    " "   " " " "       " "    "*"        " "        " "        
4  ( 1 ) "*"    "*"   " "    "*"   " " " "       " "    "*"        " "        " "        
5  ( 1 ) "*"    "*"   " "    "*"   "*" " "       " "    "*"        " "        " "        
6  ( 1 ) "*"    "*"   "*"    "*"   "*" " "       " "    "*"        " "        " "        
7  ( 1 ) "*"    "*"   "*"    "*"   "*" " "       " "    "*"        "*"        " "        
8  ( 1 ) "*"    "*"   "*"    "*"   "*" " "       " "    "*"        "*"        " "        
         RegionWest
1  ( 1 ) " "       
2  ( 1 ) " "       
3  ( 1 ) " "       
4  ( 1 ) " "       
5  ( 1 ) " "       
6  ( 1 ) " "       
7  ( 1 ) " "       
8  ( 1 ) "*"

`Application Exercise`

Perform forward stepwise and backward stepwise on the penguins dataset to find the best model to predict weight.

Chapter 6 Part 1

Setup

Recall

Why

Three classes of models

Today example: Credit

Subset Selection

Best Subset Selection

Example - Credit Data

Extensions

Best Subsets - not tidymodels

Best Subsets

Best Subsets - function

Best Subsets - Graph

Best Subsets - Best Values

Best Subsets - Looking at model

`Application Exercise`

Stepwise Selection

Forward Stepwise Selection

Forward Stepwise Selection

Forward Stepwise Selection

Forward Stepwise Credit Example

Backward Stepwise Selection

Backward Stepwise Selection

Backward Stepwise Selection

Choosing an Optimal Model

Estimating Test error

\(C_p\), AIC, BIC, Adjusted \(R^2\)

\(C_p\), AIC, BIC, Adjusted \(R^2\)

Mallow’s \(C_p\)

Akaike information criterion (AIC)

Bayesian information criterion (BIC)

Adjusted \(R^2\)

Validation and CV Sets

Compare Credit Example

Forward Stepwise in R

Backward Stepwise in R

`Application Exercise`