II.
LibraryProcess overview
Reference · livelib-process:data-science-ml--experiment-planning
experiment-planning overview
Experiment Planning and Hypothesis Testing - Design ML experiments with clear hypotheses, establish statistical test criteria, plan A/B test configurations, and define success metrics with iterative learning loops.
Attributes
displayName
experiment-planning
description
Experiment Planning and Hypothesis Testing - Design ML experiments with clear hypotheses,
establish statistical test criteria, plan A/B test configurations, and define success metrics with iterative learning loops.
libraryPath
library/specializations/data-science-ml/experiment-planning.js
specialization
data-science-ml
references
- - Rules of Machine Learning - Google: https://developers.google.com/machine-learning/guides/rules-of-ml - Experimentation Best Practices: https://developers.google.com/machine-learning/guides/rules-of-ml - A/B Testing Guidelines - Microsoft: https://exp-platform.com/ - Statistical Power Analysis: https://www.stat.ubc.ca/~rollin/stats/ssize/
example
const result = await orchestrate('specializations/data-science-ml/experiment-planning', {
projectName: 'Recommendation Engine Improvement',
experimentGoal: 'Improve click-through rate by introducing collaborative filtering',
baselineModel: 'content-based-recommender-v1',
targetMetric: 'click_through_rate',
confidenceLevel: 0.95
});
usesAgents
- general-purpose
Outgoing edges
lib_applies_to_domain1
- domain:data-science·DomainData Science
lib_belongs_to_specialization1
- specialization:data-science-ml·Specialization
lib_implements_workflow1
- workflow:data-pipeline-deployment·WorkflowData Pipeline Deployment
lib_involves_role1
- role:data-scientist·RoleData Scientist
Incoming edges
None.