Kernel SVM (ML_CN_4) This functionality uses a kernel function to map the input data to a higher-dimensional space, where a linear decision boundary is created based on the support vectors.
Kernel SVM works by finding a line or a hyperplane that separates the data into different classes. However, in cases where the data is not linearly separable, a kernel function is used to transform the data into a higher-dimensional space where it becomes separable.
Kernel SVM is a powerful algorithm that can handle complex and nonlinear data. It is widely used in image recognition, natural language processing, and other fields where the data is not easily separable in a linear fashion.
Sample Request
Build a Kernel SVM model named, "ClassicModel"
Copy {
"project_id" : 1 ,
"parent_id" : 7 ,
"block_id" : 8 ,
"function_code" : "ML_CN_4" ,
"args" : {
"model_name" : "ClassicModel" ,
"kernel" : "rbf" ,
"random_state" : 0
}
}
Building a Kernel SVM model
Kernel SVM
POST
https://autogon.ai/api/v1/engine/start
Request Body
200 Statemanagement object
Copy {
"status" : "true" ,
"message" : {
"id" : 8 ,
"project" : 1 ,
"block_id" : 8 ,
"parent_id" : 7 ,
"dataset_url" : "" ,
"x_value_url" : "" ,
"y_value_url" : "" ,
"x_train_url" : "" ,
"y_train_url" : "" ,
"x_test_url" : "" ,
"y_test_url" : "" ,
"output" : "{\"ClassicModel\": {\"function_code\": \"ML_R_3\", \"model_url\": ""}}"
}
}
Python Node
Copy const project_id = 1
const parent_id = 7
const block_id = 8
kernelSvm = await client .kernel_svm (project_id , parent_id , block_id , {
model_name : "ClassicModel" ,
kernel : "rbf" ,
random_state : 0
});
Sample Request
Make predictions with the pre-built model passing an optional test data.
Copy {
"project_id" : 1 ,
"parent_id" : 8 ,
"block_id" : 9 ,
"function_code" : "ML_CN_4_P" ,
"args" : {
"model_name" : "ClassicModel" ,
"test_data" : ""
}
}
Predicting with Kernel SVM
Kernel SVM Predict
POST
https://autogon.ai/api/v1/engine/start
Request Body
200: OK Statemanagement object
Copy {
"status" : "true" ,
"message" : {
"id" : 9 ,
"project" : 1 ,
"block_id" : 9 ,
"parent_id" : 8 ,
"dataset_url" : "" ,
"x_value_url" : "" ,
"y_value_url" : "" ,
"x_train_url" : "" ,
"y_train_url" : "" ,
"x_test_url" : "" ,
"y_test_url" : "" ,
"output" : "{\"y_pred_url\": ""}"
}
}
Python Node
Copy const project_id = 1
const parent_id = 7
const block_id = 8
kernelSvmPredict = await client .kernel_svm_predict (project_id , parent_id , block_id , {
model_name : "ClassicModel" ,
test_data : ""
});
Sample Request
Evaluate model metrics
Copy {
"project_id" : 1 ,
"parent_id" : 8 ,
"block_id" : 9 ,
"function_code" : "ML_CN_4_M" ,
"args" : {
"model_name" : "ClassicModel"
}
}
Kernel SVM Metrics
POST
https://autogon.ai/api/v1/engine/start
Request Body
200: OK StateManagement object
Copy {
"status" : "true" ,
"message" : {
"id" : 1 ,
"project" : 12 ,
"block_id" : 10 ,
"parent_id" : 11 ,
"dataset_url" : "" ,
"x_value_url" : "" ,
"y_value_url" : "" ,
"x_train_url" : "" ,
"y_train_url" : "" ,
"x_test_url" : "" ,
"y_test_url" : "" ,
"output" : "{'confusion_matrix': '', 'accuracy': 0.9}"
}
}
Python Node
Copy const project_id = 1
const parent_id = 7
const block_id = 8
kernelSvmMetrics = await client .kernel_svm_metrics (project_id , parent_id , block_id , {
model_name : "SimpleModel" ,
});