Tabular data: label delay and interpretability

Repository for the paperEvaluating the Efficacy of Instance Incremental vs.Batch Learning in Delayed Label Environments: An Empirical Study on Tabular Data Streaming for Fraud Detection

In delayed settings, is instance incremental learning the best option regarding predictive performance and computational efficiency?

No delay situation: AUCROC Agrawal gradual drift (Agr_g)

Delay of average 70k instances: AUCROC Agrawal gradual drift (Agr_g)

Usage

Prerequisites

Create a new python environment, install the requirements: pip install -r requirements.txt

Replicating results of the Generated benchmark

1. Clone this repository of your machine
2. Run the Notebook for reproducing results

NB: To use the notebook, you will need to install it in the python environment you have created using pip for example

Run your own experiments on the Generated benchmark

1. Please download first datasets (of interest) using the link here and place them in Datasets folder.
2. For example in python main.py --n_delays 0 1000 70000 --static_optim_ntrial 30 --model_name DT --dataset_name sea_g --init_fit_ratio 0.1 --n_windows 10000

• n_delays: indicates the average label delay, generated following a Poisson distribution of mean 0, 1000, 70000 respectively
• static_optim_ntrial: indicates the number of trial for tuning parameters offline (before the stream evaluation)
• model_name: the model name (e.g., DT is for Decision Tree)
• dataset_name: the dataset name (sea_g here)
• init_fit_ratio: the fraction of the dataset used for the offline optimization (0.1 in the above example)
• n_windows: average number of instances (following a Poisson distribution) in each evaluation batch (10000 for this example)

Run your own experiments of the Fraud dataset