Commands model:random_forest_regressor/train

[ALPHA] Build Random Forests Regressor model.

POST /v1/commands/

GET /v1/commands/:id

Request

Route

POST /v1/commands/

Body

name:

model:random_forest_regressor/train

arguments:

model : <bound method AtkEntityType.__name__ of <trustedanalytics.rest.jsonschema.AtkEntityType object at 0x7f9e68702090>>

Handle to the model to be used.

frame : <bound method AtkEntityType.__name__ of <trustedanalytics.rest.jsonschema.AtkEntityType object at 0x7f9e686f3fd0>>

A frame to train the model on

label_column : unicode

Column name containing the label for each observation

observation_columns : list

Column(s) containing the observations

num_trees : int32 (default=1)

Number of tress in the random forest

impurity : unicode (default=variance)

Criterion used for information gain calculation. Supported values “variance”

max_depth : int32 (default=4)

Maximum depth of the tree

max_bins : int32 (default=100)

Maximum number of bins used for splitting features

seed : int32 (default=2145878081)

Random seed for bootstrapping and choosing feature subsets

categorical_features_info : None (default=None)

<Missing Description>

feature_subset_category : unicode (default=None)

Number of features to consider for splits at each node. Supported values “auto”, “all”, “sqrt”,”log2”, “onethird”

Headers

Authorization: test_api_key_1
Content-type: application/json

Description

Creating a Random Forests Regressor Model using the observation columns and label column.

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

Values of the Random Forest Classifier model object storing:

the list of observation columns on which the model was trained,

the column name containing the labels of the observations,

the number of decision trees in the random forest,

the number of nodes in the random forest,

the map storing arity of categorical features,

the criterion used for information gain calculation,

the maximum depth of the tree,

the maximum number of bins used for splitting features,

the random seed used for bootstrapping and choosing feature subset.