A Perspective on the Data

1. A Perspective on the Data Ajit Paul Singh M.Sc. Candidate Dept. of Computing Science University of Alberta

2. Machine Learning • Systems that use experience to improve at a given task. • Data as experience • Supervised vs. Unsupervised Learning SNP focus: supervised learning

3. The Running Example

4. Data Assumptions • Samples are independent, and identically distributed (IID) • Dealing with patients/tuples • One set  complex distribution  more training data • Split into subsets  many simpler distribution  less training data per problem

5. Defining the Task • Predictive • Diagnosing members of the public • Rare class issue • Diagnosing clinic referrals • Is the training set representative of patients that will be tested ? • Subtyping cancer patients • Feature Selection • Find interesting SNPs for further study

6. Measuring Improvement • Competitors • Human experts using clinical data • Diagnostic tests (e.g. BRCA1 truncations) • Other learners using genetic markers • Benefits of Polyomx • Accuracy, Cost, Speed • Need for a baseline to compare against

7. Issues to Consider • Missing data • Negative control features

8. Types of Missing Data • Missing Completely At Random (MCAR) • Missing At Random (MAR) • Censored

9. Negative Control Features • SNPs were hand selected • Feature selection problem • Measuring relevance of selected features • Prediction problem • Ensuring the learner is robust • Add negative control features • Features that are probably irrelevant