3. •Find subgroups that exist in data
•Understand subgroup differences
Clustering
•Identify associations between predictors and
an outcome
Predictor
Identification
•Predict outcome/group assignment of new
observations based on a set of predictors
Forecasting/
Classifying
4. Violation of
typical model
assumptions
(inaccurate model
estimates)
Computational issues
with large
dimensionality (lots of
variables)
Low power to
detect significant
associations
• Bad
prediction
• Little insight
• Computability
issues
• Inaccurate
estimates
5. Topology provides a feature engineering and unsupervised
learning method robust to sample size, which provide
good input features for tree-based predictive models.