Use this to select different options to be used in constructing the K Nearest Neighbours and the displayed output.

## Display

Specifies which items of output are to be displayed in the Output window.

Similarities |
The symmetric matrix of similarities between observations. This may be very large. |

Abbreviated similarities |
This reduces the printing of the similarity matrix to just the first decimal digit (available only when Similarities are selected). |

Data summary |
The list of data variables and their means, minima, maxima and test types. |

Cross-validation errors |
The cross-validation error for all combination of options provided. If the Data to predict is a factor, this is a mean squared error. If is a factor, it is the percentage of observations for which the predictions and observed values do not match. |

Confusion matrix |
The percentage of observations for each observed group allocated to the predicted groups using the optimal combination of options. |

Predictions |
The predicted values for the observations from the rest of the observations using the optimal combination of options. |

## Calculate predictions of variates using

This setting controls how the values of the neighbours are summarized when the **Data to predict ** is a variate.

Medians |
The median of the neighbour’s values will be used. |

Means |
The mean of the neighbour’s values will be used. |

## Number of simulations

Specifies the number of times the data is split into random cross-validation groups. Increasing this will increase precision but slow down the analysis.

## Number of cross-validation groups

Specifies the number of cross-validation groups into which the data are randomly split. Values between 5 and 10 (the default) are reasonable. If this is too low, the cross-validation error may be lower than could be achieved using a full training set, but if set too high the cross-validation error may not reflect the variation in the data set.

## Seed for randomization

This gives a seed to initialize the random number generation used for the random selections of variates and units. Using zero initializes this from the computer’s clock, but specifying an nonzero value gives a repeatable analysis.

## Defaults

Reset the settings in the dialog to what they were on first opening the dialog.

## Action Icons

Clear |
Clear all fields and list boxes. | |

Help |
Open the Help topic for this dialog. |

## See also

- K Nearest Neighbours menu.
- K Nearest Neighbours Store dialog.
- K Nearest Neighbours Predictions dialog.
- Form similarity matrix menu.
- Canonical variates analysis menu.
- Stepwise Discriminant Analysis menu.
- Classification Trees menu.
- Regression Trees menu.
- Random Classification Forest menu.
- Random Regression Forest menu.
- Multivariate Analysis of Distance menu.
- Hierarchical Cluster Analysis menu.
- KNNTRAIN procedure.
- KNEARESTNEIGHBOURS procedure.
- FSIMILARITY directive for forming similarity matrices.