# 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, Adjusted-R2, or Mallow’s Cp. Or we can directly measure the predictive accuracy with cross-validation. We can also use stepwise selection, but I wouldn’t recommend it.

### 1. Selecting an algorithm

1. 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?
2. 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.
1. Laws have a certain structure
2. Algorithms are designed to find the best model with a certain structure.
3. Determine which structure scientists believe the law has and then use the algorithm that searches for models with that structure.
4. 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.
3. 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.
4. In these situations, you can return to optimization: select the model that performs the best.

### 2. Predictive Accuracy

1. 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.
2. To measure the predictive accuracy of a model, use the model to predict the values of your observations.
1. Quantify the error with mean squared error.
3. However, this approach has a flaw. It overestimates the predictive accuracy of your model.
1. 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.
2. Data scientists refer to this distinction as training error and test error.
1. 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.
2. 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.
4. Test error provides the best measure of a model’s predictive accuracy.
1. Why should this be? Because you can arbitrarily decrease any model’s training error to zero by making the model more flexible.
1. graphical depiction
2. 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.
1. bias variance decomposition
2. degrees of freedom
3. the “sweet spot” of flexibility will change from law to law. You can estimate the best model for your data with cross validation.

1. ## Cross Validation

2. 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.
3. 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.
4. This is the general outline of cross validation, but the details can vary. You can decide
1. How many points to leave out as a test set
2. How many times to repeat the process to guard against variability in the estimates

### 4. Estimates of predictive accuracy

1. 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.