Commands model:k_means/train

[BETA] Creates KMeans Model from train frame.

POST /v1/commands/

GET /v1/commands/:id

Request

Route

POST /v1/commands/

Body

name:

model:k_means/train

arguments:

model : Model

<Missing Description>

frame : Frame

A frame to train the model on.

observation_columns : list

Columns containing the observations.

column_scalings : list

Column scalings for each of the observation columns. The scaling value is multiplied by the corresponding value in the observation column.

k : int32 (default=2)

Desired number of clusters. Default is 2.

max_iterations : int32 (default=20)

Number of iterations for which the algorithm should run. Default is 20.

epsilon : float64 (default=0.0001)

Distance threshold within which we consider k-means to have converged. Default is 1e-4. If all centers move less than this Euclidean distance, we stop iterating one run.

initialization_mode : unicode (default=k-means||)

The initialization technique for the algorithm. It could be either “random” to choose random points as initial clusters, or “k-means||” to use a parallel variant of k-means++. Default is “k-means||”.

Headers

Authorization: test_api_key_1
Content-type: application/json

Description

Creating a KMeans Model using the observation columns.

Response

Status

200 OK

Body

Returns information about the command. See the Response Body for Get Command here below. It is the same.

GET /v1/commands/:id

Request

Route

GET /v1/commands/18

Body

(None)

Headers

Authorization: test_api_key_1
Content-type: application/json

Response

Status

200 OK

Body

dict

dictionary

A dictionary with trained KMeans model with the following keys:

‘cluster_size’ : dictionary with ‘Cluster:id’ as the key and the corresponding cluster size is the value ‘within_set_sum_of_squared_error’ : The set of sum of squared error for the model.