1. Konstantina Christakopoulou Liang Zeng Group G21
Big Data: New Tricks for Econometrics Varian, Hal R. "Big data: New tricks for econometrics." The Journal of Economic Perspectives (2014): 3-27.
Konstantina Christakopoulou
Liang Zeng
Group G21
Related to the Chapter 28:
Data Mining

2. Motivation. Machine Learning for Economic Transactions:
Linear Regression is not Enough!
Big data size
A lot of features: Choose variables
Relationships are not
only linear!!

3. Connection to the Course: Decision Trees e.g ID3
Challenges of ID3:
Cannot handle continuous attributes
Prone to outliers
1. C4.5, Classification And Regression Trees (CART) can handle:
+ continuous and discrete attributes
+ handle missing attributes
+ over-fitting by post-pruning
2.  Random Forests: Ensemble of decision stumps. Randomization (choosing sample + choosing attributes) leads to better accuracy!

4. ID3 Decision Tree

5. Classification and Regression Trees(CART)
Classification tree is when the predicted outcome is the class to which the data belongs.
Regression tree is when the predicted outcome can be considered a real number (e.g. the age of a house, or a patient’s length of stay in a hospital).

6. Classification and Regression Trees(CART)
Predict Titanic survivors using age and class

7. Classification and Regression Trees(CART)
A CART for Survivors of the Titanic using R language

8. Random Forests

9. Random Forests Decision Tree Learning + Many decision trees + One Tree
Choose a bootstrap sample and start to grow a tree
At each node:
Choose random sample of predictors to make the next decision
Repeat many times to grow a forest of trees
For prediction: have each tree make its prediction and then a majority vote.
Decision Tree Learning
+ Many decision trees
+ One Tree
+ Each DT on a random subset of samples
+ On all learning samples
+ Reduce the effect of outliers (no overfitting)
+ Prone to distortions e.g outliers
Random Forest

10. Boosting, Bagging, Bootstrap
Randomization can help!
Bootstrap: choose (with replacement) a sample
Bagging: averaging across models estimated with several bootstraps
Boosting: repeated estimation where misclassified observations are given an increasing weight. Final is an average

11. Thank you!