Support Vector Regression (ML_R_4)
This function can be used for solving both linear and non-linear problems.
It is based on the concept of support vectors, which are the data points that are closest to the decision boundary, or the line that separates the data into different classes. SVR algorithm aims to find the best line that maximizes the margin between the support vectors and the decision boundary, this line is known as the support vector.
This function aims to find the best line that maximizes the margin between the support vectors and the decision boundary, this line is known as the support vector. It can also be used with kernel functions to find the best decision boundary in non-linear regression problems.
It is a powerful algorithm that is able to handle high dimensional and non-linear data, making it suitable for various regression problems.
Sample Request
Build a multiple linear regression model named, "SimpleModel"
Building a Support Vector Regression model
Support Vector Regression
POST
https://autogon.ai/api/v1/engine/start
Request Body
Sample Request
Make predictions with the pre-built model passing an optional test data.
Predicting with Support Vector Regression
Support Vector Regression Predict
POST
https://autogon.ai/api/v1/engine/start
Request Body
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