Support Vector Machine (ML_CN_3)

This functionality creates a decision boundary based on the support vectors, and classifies new input data based on which side of the boundary it falls on.

In SVM, the algorithm finds the best possible line (or hyperplane in higher dimensions) that can separate two classes of data. It does this by identifying the data points closest to the dividing line, which are called support vectors, and maximizing the margin between the two classes.

Once the best line is identified, it can be used to predict the class of new data points. SVM is a powerful algorithm because it can work well with both linearly separable and non-linearly separable data by using a technique called kernel trick to transform the data into a higher dimensional space where it can be more easily separated.

Sample Request

Build a SVM model named, "ClassicModel"

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_CN_3",
    "args": {
        "model_name": "ClassicModel"
    }
}

Building a Support Vector Machine model

Support Vector Machine

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 "linear")

random_state

int

Controls the randomness of the estimator (defaults to 0).

{
    "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\": ""}}"
    }
}

// Some code

const project_id = 1
const parent_id = 7
const block_id = 8

supportVectorMachine = await client.support_vector_machine(project_id, parent_id, block_id, {
    model_name: "ClassicModel",
    kernel: "linear",
    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_3_P",
    "args": {
        "model_name": "ClassicModel",
        "test_data": ""
    }
}

Predicting with Support Vector Machine

Support Vector Machine 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

{
    "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\": ""}"
    }
}

// Some code

const project_id = 1
const parent_id = 7
const block_id = 8

supportVectorMachinePredict = await client.support_vector_machine_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_3_M",
    "args": {
        "model_name": "ClassicModel"
    }
}

Support Vector Machine 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

{
    "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}"
    }
}

// Some code

const project_id = 1
const parent_id = 7
const block_id = 8

supportVectorMachineMetrics= await client.support_vector_machine_metrics(project_id, parent_id, block_id, {
    model_name: "SimpleModel",

});

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Last updated 2 years ago

hashtagSample Request

hashtagBuilding a Support Vector Machine model

hashtagSupport Vector Machine

hashtagRequest Body

hashtagSample Request

hashtagPredicting with Support Vector Machine

hashtagSupport Vector Machine Predict

hashtagRequest Body

hashtagSample Request

hashtagSupport Vector Machine Metrics

hashtagRequest Body

Sample Request

Building a Support Vector Machine model

Support Vector Machine

Request Body

Sample Request

Predicting with Support Vector Machine

Support Vector Machine Predict

Request Body

Sample Request

Support Vector Machine Metrics

Request Body