Commands model:random_forest_classifier/train¶
[ALPHA] Build Random Forests Classifier model.
POST /v1/commands/¶
GET /v1/commands/:id¶
Request¶
Route
POST /v1/commands/
Body
name: | model:random_forest_classifier/train |
---|---|
arguments: | model : Model
frame : Frame
label_column : unicode
observation_columns : list
num_classes : int32 (default=2)
num_trees : int32 (default=1)
impurity : unicode (default=gini)
max_depth : int32 (default=4)
max_bins : int32 (default=100)
seed : int32 (default=-1236386969)
categorical_features_info : dict (default=None)
feature_subset_category : unicode (default=None)
|
Headers
Authorization: test_api_key_1
Content-type: application/json
Description
Creating a Random Forests Classifier 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
- dictionary
- A dictionary with trained Random Forest Classifier model with the following keys: |‘observation_columns’: the list of observation columns on which the model was trained, |‘label_column’: the column name containing the labels of the observations, |‘num_classes’: the number of classes, |‘num_trees’: the number of decision trees in the random forest, |‘num_nodes’: the number of nodes in the random forest, |‘feature_subset_category’: 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 feature subset.