5 Steps to Tackle Real-World Imbalanced Data
Author(s): Snehal Nair Originally published on Towards AI. Baseline Model without resampling Working with imbalanced data can be very challenging. Imbalanced data refers to data where classes do not have equal weight. Some examples of imbalanced datasets include fraud detection, churn prediction, …
Evaluating Synthetic Data Using Machine Learning
Author(s): Varatharajah Vaseekaran Originally published on Towards AI. Adversarial validation to evaluate synthetic data “Poor accuracy score” is a phrase that might cause nightmares for many data science professionals when building machine learning models for classification problems. However, a poor accuracy score …
How to Handle Imbalanced Data in ML Classification using Python
Author(s): Muttineni Sai Rohith Originally published on Towards AI. In this article, we will discuss what is Imbalanced Data, the Metrics we should use to evaluate the model with Imbalanced Data, and the Techniques used to Handle Imbalanced Data. While doing binary …
Credit Card Fraud Detection in R: Best AUC Score 99.2%
Author(s): Mishtert T Originally published on Towards AI. Fraud U+007C Anomaly Detection Light GBM Model & Synthetic data points in an imbalanced dataset What’s Light GBM Light GBM is a high-performance gradient boosting (GBDT, GBRT, GBM or MART) framework and is used …
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