Lecture 2, Decision Trees
Tid: Torsdag 4 september 2014 kl 10:00 - 12:00
Plats: V2
Aktivitet: Föreläsning
Lärare: Atsuto Maki ()
Studentgrupper: TCSCM_CSCA_1, TCSCM_CSCD_1, TCSCM_CSCE_1, TCSCM_CSCG_1, TEBSM_1, TITMM_2, TIVNM_HCID_1, TKOMK_3, TMAIM_MAIB_1, TSCRM_1, TSCRM_2, TTMAM_1, TTMAM_2
- What is a Decision Tree?
- When are decision trees useful?
- How can one select what questions to ask?
- What do we mean by Entropy for a data set?
- What do we mean by the Information Gain of a question?
- What is the problem of overfitting? Minimizing training error?
- What extensions will be possible for improvement?
Related reading:
Chapter 8.1 from An Introduction to Statistical Learning (Springer, 2013)
Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Available online: http://www-bcf.usc.edu/~gareth/ISL/