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Table of Contents

Classifier Directory

This directory is used to train and tune the model in order to produce pre-trained model and feed to backend.

Input

Benchmark

model_benchmark.py is used to benchmark different models. Once executed, it will train the models by using the provided features and hyper parameters, evaluate the model with a variety of metrics, export feature importance image of each model, dump fitted models for further analysis.

Training

The models includes:

  • random forest
  • xgboost
  • catboost
  • xgboost random forest
  • lgbm
  • Voting classifier

Evaluation

Evaluation Metrics includes:

  • accuracy
  • precision
  • recall
  • f1
  • auc
  • block_rate
  • fraud_rate
  • conversion_rate
  • average_precision

Feature Importance

Feature Importance results of each model (except voting) are stored under feature_importance directory.

Model Dump

Trained models are dumped under pretrained_models directory for further analysis.

Tuning

Optuna is used to tune the hyperparams of the listed models. random_forest_optuna_tuning.ipynb demonstrates an example of how we tune the model.

Tuning results are store under model_tuning directory. optuna_results.ipynb gives a report of the tuning interpretation.