Experimental Outlier Detection with Machine Learning

Develop a machine learning workflow for intelligent outlier detection in scientific experimental datasets, specifically targeting research with high measurement variability. Implement an ensemble approach using Isolation Forest, Local Outlier Factor, and statistical z-score methods. Create a configurable Python script that can dynamically adjust sensitivity thresholds based on domain-specific requirements and provide interpretable visualization of anomalous data points.