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Lecture 2, Decision Trees

Tid: Torsdag 3 september 2015 kl 17:00 - 19:00 2015-09-03T17:00:00 2015-09-03T19:00:00

Kungliga Tekniska högskolan
HT 2015

Plats: E1

Aktivitet: Föreläsning

Lärare: Atsuto Maki ()

Studentgrupper: TCSCM_CSCA_2, TCSCM_CSCD_2, TCSCM_CSCE_2, TCSCM_CSCG_2, TEBSM_INMV_1, TEBSM_INSR_1, TITMM_2, TIVNM_DITS_2, TIVNM_HCID_1, TKOMK_3, TMAIM_1, TSCRM_1, TSCRM_2, TTMAM_1

Detaljer (TimeEdit): Flyttad från kl 13-15 pga för liten sal

Info:

Topics:

  • 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 24 augusti 2015
Lärare Atsuto Maki redigerade 28 augusti 2015

FöreläsningLecture 2, Decision Trees


* 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 TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/¶

Lärare Atsuto Maki redigerade 28 augusti 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?
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 28 augusti 2015

Topics:¶


* 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 TibshiraniAvailable online: http://www-bcf.usc.edu/~gareth/ISL/

Schemahandläggare redigerade 1 september 2015

Torsdag 3 september 2015 kl 137:00 - 159:00

B3E1

Flyttad från kl 13-15 pga för liten sal

Lärare Atsuto Maki redigerade 2 september 2015

Topics:


* 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 3 september 2015

Topics:


* 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 27 oktober 2015

Topics:


* 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/

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