Similarity based ML
k-Nearest Neighbor, Support Vector Machines, One-Classification SVM, Logistic Regression
Last updated
k-Nearest Neighbor, Support Vector Machines, One-Classification SVM, Logistic Regression
Last updated
*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.
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.
For classification, select C-classification of nu-classification for the type under Arguments. For regression, select eps-regression or nu-regression.
X
Input variables
One-classification SVM is used for the novelty detection. It uses the method 'one-classification' in the svm function.
X
Independent variables
Y
Dependent categorical variable
R-Flow Task Example Video: Support Vector Machines
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
