The describe_model() function lists the following details of a model:
- Model name provided while saving the model.
- Description provided while saving the model.
- Algorithm.
- Prediction type.
- Target column, if any.
- Project provided while saving the model.
- Entity target.
- Name of the engine that generated the model.
- Name of the client that generated the model.
- The access level set for the model.
- The status of the model.
- The time required to run the model generating function.
- The name of the user who created the model.
- The date and time when the model was saved.
- The arguments that were passed to the model generating function.
- The input details, if they were saved.
- The output names and the names of the underlying tables which are the actual model tables for the model saved.
The required argument name, which is also the only argument, specifies the name of the model to list the details for.
A user can only describe the models that he or she has access to.
Example Prerequisites
Follow the steps in save_model() to create a classification tree model that can be input to DecisionForestPredict and save the generated model.Example
List all details of the saved model 'decision_forest_model'.
>>> # List all details of recently saved model 'decision_forest_model'. >>> describe_model(name="decision_forest_model") *** 'decision_forest_model': Model Details *** ModelName decision_forest_model ModelDescription Decision Forest test ModelAlgorithm DecisionForest ModelPredictionType CLASSIFICATION ModelTargetColumn homestyle ModelProject None ModelEntityTarget None ModelGeneratingEngine ML Engine ModelGeneratingClient teradataml ModelAccess Private ModelStatus In-Development ModelBuildTime 0 ModelLocation Advanced SQL Engine CreatedBy ALICE CreatedDate 2020-05-17 23:59:48.740000 *** 'decision_forest_model': Model Attributes *** AttrName AttrValue 0 mtry_seed 100 1 nodesize 1 2 mtry 3 3 outofbag False 4 seed 100 5 display_num_processed_rows False 6 ntree 50 7 formula homestyle ~ driveway + recroom + fullbase + ga... 8 maxnum_categorical 20 9 max_depth 12 10 categorical_encoding graycode 11 tree_type classification 12 variance 0 13 tree_size 100 *** 'decision_forest_model': Model Training Data *** InputName InputTableName NRows NCols 0 data "ALICE"."housing_train" 492 14 *** 'decision_forest_model': Model Training Objects *** OutputName OutputTableName 0 monitor_table "ALICE"."ml__td_decisionforest1_1589794611679956" 1 predictive_model "ALICE"."ml__td_decisionforest0_1589787736719763" 2 output "ALICE"."ml__td_sqlmr_out__1589787498246673"