What is next?

Questions for discussion

The below questions for discussion can help consolidate your learning. How many can you answer?

1. What is the “right” way to split data for machine learning?
   • Would that work for time-series data?
   • What if you had parameters you wanted to learn?
   • What if you wanted to compare models?
2. If we had just one number to score a model, what would you choose?
   • When is it good?
   • When is it bad?
3. How do you find out more information about a model?
4. If you run a model and get an answer, and a colleague ran an analysis independently and came to a different conclusion, how would you go about explaining the discrepancy?
5. What makes you think that the analysis is “right”? How would you know if something was wrong?
6. Did you test a hypothesis? If so which hypothesis?
   • Is there an area of your research where this is applicable?
   • What about related work on slightly different problems? Are the hypotheses the same?
7. How to handle missing data

Further topics

Here are some additional chapters to work through on a number of different topics. Choose the ones that interest you.

Logistic regression for cancer classification

Logistic regression for housing price classification

PCA of morphological traits

Prediction Alzheimer’s disease

Feature Importance Analysis

Medical image clustering

Feature scaling and principal component analysis are important parts of many data analysis pipelines.

Clustering is an unsupervised classification method.

Image Clustering uses clustering techniques to demonstrate a form of image compression.