Help Manual


Sigma Magic Help Version 17

K-Nearest Neighbors


The K-nearest neighbors (or KNN) uses the distance between the neighbors to determine which other data set(s) are the closest to the set in question and assigns the response variable based on the neighbors. The idea being that all nearby points should be similar. This algorithm is also known as supervised learning since the destination is known. The algorithm calculates the distance of the point in question to all the stored data points, and the distance values are sorted, and the k-nearest neighbors are determined. The labels of these neighbors are gathered, and a majority vote is used for classification or regression purposes. Selecting the value of k (the number of neighbors to use) will determine how well the data can be utilized to generalize the results of the kNN algorithm. A large k value reduces the variance due to noisy data but could be biased and may ignore useful patterns within the data set.

Overall, the kNN algorithm is highly unbiased and makes no a priori assumptions of the underlying data. It is simple to easy to understand and is quite popular. The disadvantage is that it is too simple and does not create any models for the data. It does not deeply understand the nature of the data and might miss useful insights at times.

To add the K-Nearest Neighbors tool to your worksheet, click on Analytics and then select KNN.


Click on Analysis Setup to open the menu options for this tool. A sample screenshot of the menu is shown below: KNN Input Menu 1
Training Set: Specify the data to be used for training the algorithms.
AutoRandom method for selection of the training data set is used./td>
RandomA random number algorithm is used to select the training set.
SequentialA sequential set of samples are picked. For example, 1st point, 3rd point, 5th point, etc.
Training Size: Specify what % of the data points should be picked for the training set. These are used to build the model, and the remaining points are used to test the model.
Probability: If this is true, the proportion of the votes for the winning class are returned as attribute prob. You can analyze the strength of the prediction using the probability values. The default value is not to request for the probability values.
Use All: This controls the handling of ties. If true, all distances equal to the kth largest are included. If false, a random selection of distances equal to the kth is chosen to use exactly k neighbors.
Num Neighbors: This is the number of neighbors that are considered to determine the classification (value of k). The default value if you do not specify a value is k = 3.
Min Votes: This is the minimum vote for a definite decision, otherwise doubt. (More precisely, less than k-l dissenting votes are allowed, even if k is increased by ties.). The default value of this variable if you don't specify this is 0.
Additional Options: This field is optional. You can specify any additional options for the R software program directly by typing it here.
Help Button: Click on the Help Button to view the help documentation for this tool.
Cancel Button: Click on the Cancel Button to discard your changes and exit this menu.
OK Button: Click on the OK Button to save your changes and try to execute the program. Note that you will need to specify the required data in order to complete the analysis and generate outputs. If there are any missing data, then the software will remind you to specify the data and click on Compute Outputs to generate analysis results.


If you click on the Data button, you will see the following dialog box. Here you can specify the data required for this analysis. Data
Search Data: The available data displays all the columns of data that are available for analysis. You can use the search bar to filter this list and to speed up finding the right data to use for analysis. Enter a few characters in the search field and the software will filter and display the filtered data in the Available Data box.
Available Data: The available data box contains the list of data available for analysis. If your workbook does not have any data in tabular format, this box will display "No Data Found". The information displayed in this box includes the row number, whether the data is Numeric (N) or Text (T), and the name of the column variable. Note that the software displays data from all the tables in the current workbook. Even though data within the same table have unique column names, columns across different tables can have similar names. Hence, it is important that you not only specify the column name but also the table name.
Add or View Data: Click on this button either to add more data into your workbook for analysis or to view more details about the data listed in the available data box. When you click on this button, it opens up the Data Editor dialog box where you can import more data into your workbook, or you can switch from the list view to a table view to see the individual data values for each column.
Required Data: The code for the required data specifies what data can be specified for that box. An example code is N: 2-4. If the code starts with an N, then you will need to select only numeric columns. If the code starts with a T, then you can select both numeric and text columns. The numbers to the right of the colon specify the min-max values. For example, if the min-max values are 2-4, then you need to select a minimum of 2 columns of data and a maximum of 4 columns of data in this box. If the minimum value is 0, then no data is required to be specified for this box.
Select Button: Click on this button to select the data for analysis. Any data you select for the analysis is moved to the right. To select a column, click on the columns in the Available Databox to highlight them and then click on the Select Button. A second method to select the data is to double click on the columns in the list of Available Data. Finally, you can also drag and drop the columns you are interested in by holding down the select columns using your left mouse key and dragging and dropping them in one of the boxes on the right.
Selected Data: If the right amount of data columns has been specified, the list box header will be displayed in the black color. If sufficient data has not been specified, then the list box header will be displayed in the red color. Note that you can double-click on any of the columns in this box to remove them from the box.
View Selection: Click on this button to view the data you have specified for this analysis. The data can be viewed either in the tablular format or you can view a graphical summary of the data selected.


If you click on the Program button, the software will display the program code - an example screenshot is shown in the figure below. Pre-Process Inputs 3
R Program: You can view the R program that will be executed here. This program is usually automatically generated from the options you have specified in the setup earlier. This is the program that will be executed by the R program to generate analysis outputs. If you like, you can edit this program.
Auto Mode: If the radio button is selected as Auto, then the software will automatically update this code based on any changes you make in the input dialog box. We recommend that you use this option to generate the R program so that all your input settings are used to generate analysis results.
Manual Mode: If you use the Manual option, then you will be allowed to edit the R program before the program is executed. Make sure that you specify a syntactically correct program; otherwise, the R program may report errors.


If you click on the Verify button, the software will perform some checks on the data you have entered. A sample screenshot of the data is shown in this figure. Pre-Process Inputs 4
Verify Checks: The objective of this analysis as well as any checks that are performed are listed in this dialog box. For example, the software may check if you have correctly specified the input options and if you have specified the data correctly for analysis.
Check Status: The results of the analysis checks are listed here. If the checks are passed, then they are shown as a green-colored checkmark. If the verification checks fail, then they are shown as a red-colored cross. If the verification checks result in a warning, they are shown in the orange color exclamation mark and finally, any checks that are required to be performed by the user are shown as blue info icons.


Click on Compute Outputs to generate the outputs from this analysis. A sample screenshot of the worksheet is shown below. KNN Example
Notes Section: The analysis results section shows a comparison of the kNN model predictions vs. the test data if test data is available. If no test data is available, the results are shown for the training data set. The cross-table contains information about the number of records in that cell, the chi-square value, and the proportion of data that falls in that cell.
Graph Section: A confusion matrix plot is also shown in the graphical portion of the display. From this plot, we can see that there were 8 instances where the predicted value was 3, and the actual value was 3, etc. Ideally, for a good fit, all the off-diagonal elements should be close to 0. The conclusion at the bottom shows the accuracy of the prediction for the training data set and the test data set (if available). The prediction column uses the kNN model that was developed to make predictions for the missing values of the response variable.


Here are a few notes regarding this analysis:
  • KNN uses the train data set to make predictions for the test set and the prediction set. However, you may want to set the training data set to 100% for predictions and not use a test set so that all the data is considered for making predictions.
  • If you want to review the R program that was used to generate this output, go to the %TMP%/Sigma Magic/ folder and look for the file smRScript.R after you execute the program. Note that this folder also contains other files such as error or warning output, text, and graphical output from running R. However, these files are stored in a temporary location and are not persistent between sessions.


Following examples can be found in the Examples folder within the software.
  • Use the KNN method to estimate missing values in the gear column (KNN 1.xlsm).


For more information on this topic, please refer to the following articles. Do note that if any external links are mentioned below, they are for reference purposes only.