Explore how handling class imbalance in modeling affects different classification metrics using a dataset on predicting damage from aircraft strikes with wildlife. Learn about data overview, exploratory data analysis, model building, cross-validation, sensitivity, preprocessing, and imbalance workflow. Discover techniques like numeric variable analysis, pears plot, bagtree modeling, and balancing results to improve classification performance.
Overview
Syllabus
Introduction
Data
Test Data
Data Overview
numeric variables
exploratory data analysis
pears plot
build model
crossvalidation
sensitivity
preprocessing
modeling
unknown
bagtree
imbalance workflow
cross validation
sensitivity and specificity
balanced result
conclusion
Taught by
Julia Silge
