Table Of Contents

Commands model:random_forest_classifier/new

Create a ‘new’ instance of a Random Forest Classifier model.

POST /v1/commands/

GET /v1/commands/:id

Request

Route

POST /v1/commands/

Body

name:

model:random_forest_classifier/new

arguments:

dummy_model_ref : Model

<Missing Description>

name : unicode (default=None)

User supplied name.


Headers

Authorization: test_api_key_1
Content-type: application/json

Description

Random Forest [1] is a supervised ensemble learning algorithm which can be used to perform binary and multi-class classification. The Random Forest Classifier model is initialized, trained on columns of a frame, used to predict the labels of observations in a frame, and tests the predicted labels against the true labels. This model runs the MLLib implementation of Random Forest [2]. During training, the decision trees are trained in parallel. During prediction, each tree’s prediction is counted as vote for one class. The label is predicted to be the class which receives the most votes. During testing, labels of the observations are predicted and tested against the true labels using built-in binary and multi-class Classification Metrics.

footnotes

[1]https://en.wikipedia.org/wiki/Random_forest
[2]https://spark.apache.org/docs/1.5.0/mllib-ensembles.html#random-forests

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

Model

A new instance of RandomForestClassifierModel