Model Selection
Chapter 4  Model Selection
Summary: What if we do not know which type of model to use? We can
select a model based on its predictive accuracy, which we can estimate
with AIC, BIC, AdjustedR2, or Mallow’s Cp. Or we can directly measure
the predictive accuracy with crossvalidation. We can also use stepwise
selection, but I wouldn’t recommend it.
1. Selecting an algorithm
 There are many different algorithms to use to fit a model to your
data. Each algorithm will return a different model. How do you know
which algorithm to use?
 The best answer is clear, but often impractical: you wish to model a
law. Choose the algorithm that comes nearest to your current
understanding of the law. If you do not understand the law, consult
with experts who do.
 Laws have a certain structure
 Algorithms are designed to find the best model with a certain
structure.
 Determine which structure scientists believe the law has and
then use the algorithm that searches for models with that
structure.
 This approach will let you profit from whatever information is
encoded in scientific consensus, but it has another benefit as
well. Most of the tests used by statisticians assume that you
have used this approach.
 However, this approach does not address the conditions that many
scientists work in. It does not work if experts do not agree on the
form of a natural law, or if you are working in a new area with
little previous research.
 In these situations, you can return to optimization: select the
model that performs the best.
2. Predictive Accuracy
 How should you measure the performance of a model? A model that
makes better predictions is more useful (and more likely to be
accurate) than a model that makes poor predictions.
 To measure the predictive accuracy of a model, use the model to
predict the values of your observations.
 Quantify the error with mean squared error.
 However, this approach has a flaw. It overestimates the predictive
accuracy of your model.
 Consider what would happen if you fit two models. In the first
model, use all of your data, including point A to fit the model.
In the second model, do not include point A. Then use both
models to predict point A. The first model will make a more
accurate prediction.
 Data scientists refer to this distinction as training error and
test error.
 training set  the set of points that you use to fit, or
train, a model. Training error is the error that your model
makes when predicting the training set.
 test set  a set of points that you do not use to fit the
model. You can use this set to estimate the model’s test
error, the error that the model makes when predicting the
values of new observations.
 Test error provides the best measure of a model’s predictive
accuracy.
 Why should this be? Because you can arbitrarily decrease any
model’s training error to zero by making the model more
flexible.
 graphical depiction
 However, at some point decreases in training error are
associated with increases in test error. You begin to fit the
noise in the data more than the law that generated the data, a
situation known as overfitting.
 bias variance decomposition
 degrees of freedom
 the “sweet spot” of flexibility will change from law to law.
You can estimate the best model for your data with cross
validation.

Cross Validation
 Cross validation is a method for measuring the test error of a
model. With cross validation, you can compare the performance of
different models and select the best one.
 To cross validate your model, divide your data into a training set
and a test set. Fit your model to the training set and then use it
to estimate the test set. Quantify the error with the mean squared
error.
 This is the general outline of cross validation, but the details can
vary. You can decide
 How many points to leave out as a test set
 How many times to repeat the process to guard against
variability in the estimates
4. Estimates of predictive accuracy
 Before modern computing made cross validation feasible,
statisticians developed other ways to estimate the predictive
accuracy of a model. These estimates can provide a second metric for
comparing models.
 Adjusted Rsquared
 Akaike Information Criteria (AIC)
 Bayesian Information Criteria (BIC)
 Mallow’s Cp
5. Summary
 Predictive accuracy provides a basis for comparing models. Models
with higher predictive accuracy yield more useful predictions and
are more likely to provide an accurate description of the underlying
law.
 Cross validation provides a straightforward, easy to understand
method for estimating predictive accuracy.