Similarity based ML

k-Nearest Neighbor, Support Vector Machines, One-Classification SVM, Logistic Regression

K-Nearest Neighbor

Input Data

*Target Data	Input data
*Model Name	Name of the model
Output Name	Name of the output after calculation(optional)
Merge Data	If not empty, the prediction data will be stored in the selected data frame(optional)
*X	Input Variable
*Y(factor)	The target variable for classification or regression. If it is not a factor type, convert it to a factor.

[Task Information of knn]

Support Vector Machines

Input Data

Parameters	Input
*Target Data	Input data
*Model Name	Name of the model
Output Name	Name of the output after calculation(optional)
Merge Data	If not empty, the prediction data will be stored in the selected data frame(optional)
*X	Input variable
*Y	Target data. Factor type for classification and numeric type for regression.

[Task Information of SVM]

For classification, select C-classification of nu-classification for the type under Arguments. For regression, select eps-regression or nu-regression.

One-Classification SVM

Input Data

X	Input variables

One-classification SVM is used for the novelty detection. It uses the method 'one-classification' in the svm function.

[Task Information of One-classification SVM]

Logistic Regression

Input Data

X	Independent variables
Y	Dependent categorical variable

[Task Information of Logistic Regression]

Workflow Example

R-Flow Task Example Video: Support Vector Machines

R Packages

K-Nearest Neighbor

Packages name: kknn, Method: kknn

https://cran.r-project.org/web/packages/kknn/index.html

Support Vector Machines/One-Classification svm

Packages name: e1071, Method: svm

https://cran.r-project.org/web/packages/e1071/index.html