AutoClassifier._init_ | AutoML | teradataml - AutoClassifier._init_ - Teradata Package for Python

Teradata® Package for Python User Guide

Deployment
VantageCloud
VantageCore
Edition
Enterprise
IntelliFlex
VMware
Product
Teradata Package for Python
Release Number
20.00
Published
March 2024
Language
English (United States)
Last Update
2024-04-09
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Product Category
Teradata Vantage

AutoClassifier is a special purpose AutoML feature to run classification specific tasks.

Optional Arguments:

  • include: Specifies the model algorithms to be used for model training phase.

    By default, all five models are used for training for regression and binary classification problem, while only three models are used for multiclass.

    Permitted values are "glm", "svm", "knn", "decision_forest", "xgboost".

  • exclude: Specifies the model algorithms to be excluded from model training phase.

    No model is excluded by default.

    Permitted values are "glm", "svm", "knn", "decision_forest", "xgboost".

  • verbose: Specifies the detailed execution steps based on verbose level.
    Permitted values are: *
    • 0: prints the progress bar and leaderboard.
    • 1: prints the execution steps of AutoML.
    • 2: prints the intermediate data between the execution of each step of AutoML.
    Default value is 0.
  • max_runtime_secs: Specifies the time limit in seconds for model training.
  • stopping_metric: Specifies the stopping metrics for stopping tolerance in model training.
    This argument is required if stopping_tolerance is set; otherwise, optional.
    Permitted values are:
    • For task_type "Regression": "R2", "MAE", "MSE", "MSLE", "RMSE", "RMSLE".
    • For task_type "Classification": 'MICRO-F1','MACRO-F1', 'MICRO-RECALL','MACRO-RECALL', 'MICRO-PRECISION', 'MACRO-PRECISION', 'WEIGHTED-PRECISION','WEIGHTED-RECALL', 'WEIGHTED-F1', 'ACCURACY'.
  • stopping_tolerance: Specifies the stopping tolerance for stopping metrics in model training.
    This argument is required if stopping_metric is set; otherwise, optional.
  • custom_config_file: Specifies the path of JSON file in case of custom run.