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Hierarchical Clustering (ML_CG_1)

Hierarchical Clustering groups similar data points into clusters by recursively merging the two closest clusters based on a distance metric.

Hierarchical Clustering is a machine learning algorithm used for grouping similar data points into clusters. It starts with each data point in its own cluster, and then recursively merges the two closest clusters until there is only one cluster left.

The algorithm uses a distance metric to determine the similarity between clusters and data points, and creates a dendrogram to visualize the hierarchy of the merged clusters.

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Sample Request

Build a Hierarchical Clustering model named, "ClusterModel"

{
    "project_id": 1,
    "parent_id": 7,
    "block_id": 8,
    "function_code": "ML_CG_1",
    "args": {
	"model_name": "ClusterModel",
        "n_clusters": 5,
        "affinity": "euclidean",
        "linkage": "ward"
    }
}

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Building a Hierarchical Clustering model

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Hierarchical Clustering

POST https://autogon.ai/api/v1/engine/start

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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.

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Sample Request

Make predictions with the pre-built model passing an optional test data.

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Predicting with Hierarchical Clustering

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Hierarchical Clustering Predict

POST https://autogon.ai/api/v1/engine/start

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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

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