# Out-of-Sample Dataset

An Out-of-Sample Dataset is a Dataset that is used in a Out-of-Sample Evaluation Task.

**Context:**- It is a dataset that is not used in a model learning task.

**Counter-Example(s):****See:**Cross-Validation, Out-of-Sample Forecasting Experiment.

## References

### 2019

- (Fomby, 2019) ⇒ Thomas B. Fomby (2019). "Out-of-Sample Forecasting Experiment" Retrieved: 2019-05-01.
- QUOTE: Out-of-sample forecasting experiments are used by forecasters to determine if a proposed leading indicator is potentially useful for forecasting a target variable. The steps for conducting an out-of-sample forecasting experiment are as follows:

- 1) Divide the available data on the target variable, [math]y_t[/math], (here we assume [math]y_t[/math] is stationary) and the proposed leading indicator,[math]x_t[/math] , (likewise we assume that [math]x_t[/math] is stationary) into two parts: the in-sample data set (roughly 80% of the data) and the out-of-sample data set (the remaining 20% of the entire data set).

### 2017

- (Sammut & Webb, 2017) ⇒ Claude Sammut, and Geoffrey I. Webb. (2017). “Out-of-Sample Data.” In: (Sammut & Webb, 2011)
- QUOTE: Out-of-sample data are data that were not used to learn a model. Holdout evaluation uses out-of-sample data for evaluation purposes.