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 : Model

Handle to the model to be used.

frame : Frame

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. Default is 1.

impurity : unicode (default=variance)

Criterion used for information gain calculation. Default supported value is “variance”.

max_depth : int32 (default=4)

Maxium depth of the tree. Default is 4.

max_bins : int32 (default=100)

Maximum number of bins used for splitting features. Default is 100.

seed : int32 (default=-1073942687)

Random seed for bootstrapping and choosing feature subsets. Default is a randomly chosen seed.

categorical_features_info : dict (default=None)

Arity of categorical features. Entry (n-> k) indicates that feature ‘n’ is categorical with ‘k’ categories indexed from 0:{0,1,...,k-1}

feature_subset_category : unicode (default=None)

Number of features to consider for splits at each node. Supported values “auto”, “all”, “sqrt”,”log2”, “onethird”. If “auto” is set, this is based on numTrees: if numTrees == 1, set to “all”; if numTrees > 1, set to “onethird”.

Headers

Authorization: test_api_key_1
Content-type: application/json

Description

Creating a Random Forests Regressor Model using the observation columns and target 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

dictionary

|A dictionary with trained Random Forest Regressor model with the following keys: |‘observation_columns’: the list of observation columns on which the model was trained |‘label_columns’: the column name containing the labels of the observations |‘num_trees’: the number of decision trees in the random forest |‘num_nodes’: the number of nodes in the random forest |‘categorical_features_info’: the map storing arity of categorical features |‘impurity’: the criterion used for information gain calculation |‘max_depth’: the maximum depth of the tree |‘max_bins’: the maximum number of bins used for splitting features |‘seed’: the random seed used for bootstrapping and choosing featur subset