View source for Models-TDSM

= Mathematical Models =


'''Properties of Models'''

<br>7-1.
Quantum physics is much more complicated than Newtonian physics. Which model passes the Occam's Razor test, and why?

[[TDSM 7.1|(Solution 7.1)]] 

<br>7-3.
Give examples of first-principle and data-driven models used in practice.

[[TDSM 7.3|(Solution 7.3)]] 

<br>7-5.
For one or more of the following <i>"The Quant Shop"</i> challenges, partition the full problem into subproblems that can be independently modeled:
* <i>Miss Universe?</i>
* <i>Movie gross?</i>
* <i>Baby weight?</i>
* <i>Art auction price?</i>
* <i>Snow on Christmas?</i>
* <i>Super Bowl / College Champion?</i>
* <i>Ghoul Pool?</i>
* <i>Future Gold / Oil Price?</i>

[[TDSM 7.5|(Solution 7.5)]] 


'''Evaluation Environments'''

<br>7-7.
Explain what precision and recall are. How do they relate to the ROC curve?

[[TDSM 7.7|(Solution 7.7)]] 

<br>7-9.
Explain what overfitting is, and how you would control for it.

[[TDSM 7.9|(Solution 7.9)]] 

<br>7-11.
What is cross-validation? How might we pick the right value of <i>k</i> for <i>k</i>-fold cross validation?

[[TDSM 7.11|(Solution 7.11)]] 

<br>7-13.
Explain why we have training, test and validation data sets and how they are used effectively?

[[TDSM 7.13|(Solution 7.13)]] 

<br>7-15.
Propose baseline models for one or more of the following <i>"The Quant Shop"</i> challenges:
* <i>Miss Universe?</i>
* <i>Movie gross?</i>
* <i>Baby weight?</i>
* <i>Art auction price?</i>
* <i>Snow on Christmas?</i>
* <i>Super Bowl / College Champion?</i>
* <i>Ghoul Pool?</i>
* <i>Future Gold / Oil Price?</i>

[[TDSM 7.15|(Solution 7.15)]] 


'''Implementation Projects'''

<br>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.

[[TDSM 7.17|(Solution 7.17)]] 


'''Interview Questions'''

<br>7-19.
What do we mean when we talk about the bias-variance tradeoff?

[[TDSM 7.19|(Solution 7.19)]] 

<br>7-21.
Which is better: having good data or good models? And how do you define "good"?

[[TDSM 7.21|(Solution 7.21)]] 

<br>7-23.
How would you define and measure the predictive power of a metric?

[[TDSM 7.23|(Solution 7.23)]] 


'''Kaggle Challenges'''

<br>7-25.
Who will win the NCAA basketball tournament?
https://www.kaggle.com/c/march-machine-learning-mania-2016

[[TDSM 7.25|(Solution 7.25)]]