2009, Metrics, Predictive Modeling and Risk

Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Domino Data Lab

APRIL 21, 2021

Rules-based fraud detection (top) vs. classification decision tree-based detection (bottom): The risk scoring in the former model is calculated using policy-based, manually crafted rules and their corresponding weights. from sklearn import metrics. from imblearn.over_sampling import SMOTE. from datetime import datetime.

Statistics

Statistics Machine Learning Modeling Metrics

Explaining black-box models using attribute importance, PDPs, and LIME

Domino Data Lab

AUGUST 1, 2021

Model distillation – this approach builds a separate explainable model that mimics the input-output behaviour of the deep network. Because this separate model is essentially a white-box, it can be used for extraction of rules that explain the decisions behind the ANN. Creating a PDP for our model is fairly straightforward.

Modeling

Modeling Deep Learning Machine Learning Knowledge Discovery

Data Leaders Brief

Credit Card Fraud Detection using XGBoost, SMOTE, and threshold moving

Explaining black-box models using attribute importance, PDPs, and LIME

Webinars

Stay Connected