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Explain the common usage of validation sets, also known as holdout sets |
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=== Validation with Existing Data ===
Validation based on existing data involves analyzing the [[goodness of fit]] of the model or analyzing whether the [[Errors and residuals|residuals]] seem to be random (i.e. [[#Residual diagnostics|residual diagnostics]]). This method involves using analyses of the models closeness to the data and trying to understand how well the model predicts its own data. One example of this method is in Figure 1, which shows a polynomial function fit to some data. We see that the polynomial function does not conform well to the data, which appears linear, and might invalidate this polynomial model.
Commonly, statistical models on existing data are validated using a validation set, which may also be referred to as a holdout set. A validation set is a set of data points that the user leaves out when fitting a statistical model. After the statistical model is fitted, the validation set is used as a measure of the model's error. If the model fits well on the initial data but has a large error on the validation set, this is a sign of overfitting, as seen in Figure 1.
[[Image:Overfitted Data.png|thumb|300px|Figure 1. Data (black dots), which was generated via the straight line and some added noise, is perfectly fitted by a curvy [[polynomial]].]]
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