Commands model:logistic_regression/train¶
[ALPHA] Build logistic regression model.
POST /v1/commands/¶
GET /v1/commands/:id¶
Request¶
Route
POST /v1/commands/
Body
name: | model:logistic_regression/train |
---|---|
arguments: | model : Model
frame : Frame
label_column : unicode
observation_columns : list
frequency_column : unicode (default=None)
num_classes : int32 (default=2)
optimizer : unicode (default=LBFGS)
compute_covariance : bool (default=True)
intercept : bool (default=True)
feature_scaling : bool (default=False)
threshold : float64 (default=0.5)
reg_type : unicode (default=L2)
reg_param : float64 (default=0.0)
num_iterations : int32 (default=100)
convergence_tolerance : float64 (default=0.0001)
num_corrections : int32 (default=10)
mini_batch_fraction : float64 (default=1.0)
step_size : float64 (default=1.0)
|
Headers
Authorization: test_api_key_1
Content-type: application/json
Description
Creating a Logistic Regression Model using the observation column and label column of the train frame.
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
An object with a summary of the trained model. The data returned is composed of multiple components:
int : numFeaturesNumber of features in the training dataint : numClassesNumber of classes in the training datatable : summaryTableA summary table composed of:Frame : CovarianceMatrix (optional)Covariance matrix of the trained model.The covariance matrix is the inverse of the Hessian matrix for the trained model. The Hessian matrix is the second-order partial derivatives of the model’s log-likelihood function.