4. Application of Machine Learning Models & Algorithms

Label	KSAC Description		Bloom's Taxonomy Level	Cross-Cutting
a	Explain evaluation metrics for machine learning algorithms (e.g., accuracy, precision/recall, ROC curves, R^2)	knowledge	2
b	Describe approaches to test for bias in data	knowledge	2
c	Explain key troubleshooting techniques for machine learning algorithms (e.g., evaluate biasvariance tradeoff, use cross-validation)	knowledge	2
d	Explain sampling methods with respect to different applications, i.e. error estimates, surveys, A/B-testing	knowledge	2
e	Train a machine learning model and use it to make predictions	skill	3
f	Perform data manipulation using appropriate tools and software	skill	3	Generalist 3a, Data Analytics 4c
g	Provide rationale for selecting the appropriate sampling methodology	skill	3	Data Analytics 5j