Models-TDSM
Mathematical Models
Properties of Models
7-1.
Quantum physics is much more complicated than Newtonian physics. Which model passes the Occam's Razor test, and why?
7-3.
Give examples of first-principle and data-driven models used in practice.
7-5.
For one or more of the following "The Quant Shop" challenges, partition the full problem into subproblems that can be independently modeled:
- Miss Universe?
- Movie gross?
- Baby weight?
- Art auction price?
- Snow on Christmas?
- Super Bowl / College Champion?
- Ghoul Pool?
- Future Gold / Oil Price?
Evaluation Environments
7-7.
Explain what precision and recall are. How do they relate to the ROC curve?
7-9.
Explain what overfitting is, and how you would control for it.
7-11.
What is cross-validation? How might we pick the right value of k for k-fold cross validation?
7-13.
Explain why we have training, test and validation data sets and how they are used effectively?
7-15.
Propose baseline models for one or more of the following "The Quant Shop" challenges:
- Miss Universe?
- Movie gross?
- Baby weight?
- Art auction price?
- Snow on Christmas?
- Super Bowl / College Champion?
- Ghoul Pool?
- Future Gold / Oil Price?
Implementation Projects
7-17.
Build a general model evaluation system in your favorite programming language, and set it up with the right data to assess models for a particular problem. Your environment should report performance statistics, error distributions and/or confusion matrices as appropriate.
Interview Questions
7-19.
What do we mean when we talk about the bias-variance tradeoff?
7-21.
Which is better: having good data or good models? And how do you define "good"?
7-23.
How would you define and measure the predictive power of a metric?
Kaggle Challenges
7-25.
Who will win the NCAA basketball tournament?
https://www.kaggle.com/c/march-machine-learning-mania-2016
