AutoFraud is a dedicated AutoML pipeline designed specifically for fraud detection tasks. It automates the process of building, training, and evaluating models tailored to identify fraudulent activities, streamlining the workflow for fraud detection use cases.
Optional Arguments
- include
- Specifies the model algorithms to be used for model training phase.
By default, all five models are used for training for this specific binary classification problem.
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 "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.
- max_models
- Specifies the maximum number of models to be trained.
- custom_config_file
- Specifies the path of JSON file in case of custom run.
- skip_phases
- Specifies the phases to be skipped during the AutoML run.Permitted values:
- "Feature_Exploration"
- "Feature_Engineering"
- "Data_Preparation"
Enable AutoML pipeline to start from any step and allow skipping major/minor steps.
Major Step Comments Feature Exploration Can be safely skipped. If skipped, statistical analysis for the provided dataset is not available. Feature Engineering If skipped, AutoML does not remove duplicates/redundant features, handle missing values, encode categorical features, or apply custom transforms. You must provide fully preprocessed numeric input during fit. Data Preparation If skipped, AutoML does not perform outlier handling, imbalance handling, scaling, or feature selection (RFE/LASSO/PCA). You must ensure training-ready features. Model Training If skipped, models are not trained and only prepared data is generated. Use AutoDataPrep for such workflows. Model Evaluation Cannot be skipped because it is required for leaderboard generation. - **kwargs
- Specifies additional arguments for AutoClassifier.
- volatile
- Specifies whether to put the interim results of the functions in a volatile table or not. When set to True, results are stored in a volatile table, otherwise not.
Default value: False
- persist
- Specifies whether to persist the interim results of the functions in a table or not. When set to True, results are persisted in a table; otherwise, results are garbage collected at the end of the session.
Default value: False
- seed
- Specifies the random seed for reproducibility.
Default value: 42
- imbalance_handling_method
- Specifies which data imbalance method to use.
Default value: SMOTE
Permitted values are "SMOTE", "ADASYN", "SMOTETomek", "NearMiss".
- enable_lasso
- Specifies whether to use lasso regression for feature selection. By default, only RFE and PCA are used for feature selection.
Default value: False
- enable_rfe
- Specifies whether to enable RFE for feature selection.
Default value: True
- enable_pca
- Specifies whether to enable PCA for feature selection.
Default value: True
- raise_errors
- Specifies whether to raise errors or warnings for non-blocking errors. When set to True, raises errors, otherwise raises warnings.
Default value: False