Tree-Based & Ensemble Learning Lab
Introduction to Machine Learning in Pharmaceutical Sciences (BP301T) · Semester 3 · Unit 4: Tree-Based Models & Ensemble Learning
Build transparent feature splits, inspect decision paths, depth, feature-importance concepts, ensemble vocabulary, and limitations.
What this unit covers
Decision trees use splitting criteria, decision paths, and feature-importance concepts. Random forests introduce an ensemble idea; they may stabilize a teaching pattern while making explanation more complex. Advantages and limitations must be stated before applying a case context.
Apply it
Describe a hypothetical first split for a fictional ADR-risk, patient-classification, or treatment-outcome teaching example and name an interpretability limitation.
How this lab works
A visible browser-native teaching interaction updates illustrative states, scores, or visual summaries from authored values.
Data used
Fictional or browser-generated teaching inputs; no patient, proprietary manufacturing, or clinical-record data.
Limits
Educational simulation only. Outputs are not clinical advice, patient-specific predictions, release specifications, regulatory decisions, or professional competency evidence.