Till KTH:s startsida Till KTH:s startsida

Lecture 2, Decision Trees

Tid: Torsdag 4 september 2014 kl 10:00 - 12:00 2014-09-04T10:00:00 2014-09-04T12:00:00

Kungliga Tekniska högskolan
HT 2014

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

Info:
  • 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?

Slides for Lecture 2

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/
Schemahandläggare skapade händelsen 2 april 2014

ändrade rättigheterna 15 maj 2014

Kan därmed läsas av alla och ändras av lärare.
Lärare Atsuto Maki redigerade 29 augusti 2014

FöreläsningLecture 2, Decision Trees

Lärare Atsuto Maki redigerade 2 september 2014


* 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?
* Bias - Variance trade-off

Lärare Atsuto Maki redigerade 3 september 2014


* 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?
* Bias - Variance trade-off
What extensions will be possible for improvement?
Slides from this lecture: Slides for Lecture 2   ¶

Lärare Atsuto Maki redigerade 4 september 2014


* 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?
Slides from this lecture: Slides for Lecture 2  

Lärare Atsuto Maki redigerade 4 oktober 2014


* 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?
Slides from this lecture: Slides for Lecture 2

Lärare Atsuto Maki redigerade 5 oktober 2014


* 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?
Slides from this lecture: Slides for Lecture 2or Lecture 2¶

Related reading:¶

An Introduction to Statistical Learning with Applications in R (Springer, 2013)Gareth James, Daniela Witten, Trevor Hastie and Robert TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/¶

Chapter 8.1¶

Lärare Atsuto Maki redigerade 5 oktober 2014


* 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?
Slides for Lecture 2

Related reading:

Chapter 8.1 from An Introduction to Statistical Learning with Applications in R (Springer, 2013)

Gareth James, Daniela Witten, Trevor Hastie and Robert TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/

Chapter 8.1¶ ¶

Lärare Atsuto Maki redigerade 15 oktober 2014


* 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?
Slides for Lecture 2

Related reading:

Chapter 8.1 from An Introduction to Statistical Learning (Springer, 2013)

Gareth James, Daniela Witten, Trevor Hastie and Robert TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/

Lärare Atsuto Maki redigerade 9 juni 2015

* 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?
Slides for Lecture 2

Related reading:

Chapter 8.1 from An Introduction to Statistical Learning (Springer, 2013)

Gareth James, Daniela Witten, Trevor Hastie and Robert TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/

Hela världen får läsa.

Senast ändrad 2015-06-09 20:25

Taggar: Saknas än så länge.