Is-this-a-good-customer
OpenML dataset with id 43442
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22102267/Is-this-a-good-customer.arff
Upload date: 23 March 2022
Dataset Characteristics
Number of classes: 0
Number of features: 14 (numeric: 10, symbolic: 0 and in total binary: 0 )
Number of instances: 1,723
Number of instances with missing values: 0
Number of missing values: 0
Context Imbalanced classes put accuracy out of business. This is a surprisingly common problem in machine learning (specifically in classification), occurring in datasets with a disproportionate ratio of observations in each class. Content Standard accuracy no longer reliably measures performance, which makes model training much trickier. Imbalanced classes appear in many domains, including:
Antifraud Antispam
Inspiration
5 tactics for handling imbalanced classes in machine learning:
Up-sample the minority class
Down-sample the majority class
Change your performance metric
Penalize algorithms (cost-sensitive training)
Use tree-based algorithms
This page was built for dataset: Is-this-a-good-customer
