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"

{
    "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

Name	Type	Description
project_id*	int	The id of the current project
block_id*	int	The id of the current block
function_code*	string	The function code for current block
parent_id*	int	The id of the previous block
args*	object	Block arguments
model_name*	String	Name of the model to be used for prediction.
kernel	String	Specifies the kernel type to be used in the algorithm (defaults to "rbf").
random_state	int	Controls the randomness of the estimator (defaults to 0).

200 Statemanagement object

{
    "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

// Some code

Node

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.

{
    "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

Name	Type	Description
model_name*	String	Name of previously trained model to be used for prediction
test_data	String	Input data for prediction. Defaults to x_train_url in StateManagment
project_id*	int	ID of the current project
block_id*	int	ID of the current block
parent_id*	int	ID of the previous block
function_code*	String	Function code for the current block

200: OK Statemanagement object

{
    "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

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

{
    "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

Name	Type	Description
project_id*	int	ID of the current project
parent_id*	int	ID of the previous block
block_id*	int	ID of the current block
function_code*	String	Function code for the current block
model_name*	String	Name of the pre-trained model to be used for evaluation

200: OK StateManagement object

{
    "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

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",

});

Node