AutoML._init_ | AutoML | teradataml - AutoML._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

AutoML is an approach that automates the process of building, training, and validating machine learning models. It involves various algorithms to automate various aspects of the machine learning workflow, such as data preparation, feature engineering, model selection, hyperparameter tuning, and model deployment. It aims to simplify the process of building machine learning models, by automating some of the more time-consuming and labor-intensive tasks involved in the process.

AutoML is designed to handle both regression and classification (binary and multiclass) tasks. You can specify the task type on the provided dataset. By default, AutoML decides the task type.

AutoML by default, trains using all model algorithms applicable for the task type problem.

For example,
  • For multiclass classification problem, only three models, "svm", "knn", "decision_forest", "xgboost", are available to train, by default. Because "glm" and "svm" does not support multiclass classification problem.
  • For regression and binary classification problem, all five models, "glm", "svm", "knn", "decision_forest", "xgboost", are available to train by default.
AutoML provides functionality to use specific model algorithms for training. You can provide either include or exclude model.
  • For include, only specified models are trained.
  • For exclude, all models except specified model are trained.

AutoML also provides an option to customize the processes within feature engineering, data preparation and model training phases. You can customize the processes by passing the JSON file path in case of custom run. It also supports early stopping of model training based on stopping metrics and maximum running time.

Optional Arguments:

  • task_type: Specifies the task type for AutoML, whether to apply regression or classification on the provided dataset.

    Set this argument to "Default", if you want AutoML to decide the task type automatically.

    Permitted values are "Regression", "Classification", "Default".

    Default value is "Default".

  • 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.