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Tree net and_randomforests_2009

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Matthew Magistrado
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Introduction to Random Forests and Stochastic Gradient Boosting Dan Steinberg Mykhaylo Golovnya [email_address] August, 2009
Initial Ideas on Combining Trees ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Past Decade Development ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],Multi Tree Methods ,[object Object],[object Object],Prediction Via Voting
Random Forest ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Randomness is introduced in order to keep correlation low ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Important to Keep Correlation Low ,[object Object],[object Object]
Randomness in Split Selection ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Performance as a Function of R ,[object Object],[object Object]
Usage Notes ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Proximity Matrix – Raw Material for Further Advances ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Post Processing and Interpretation
Introduction to Stochastic Gradient Boosting ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Predictive Modeling ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Model X f
Loss Functions ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Regression and Classification Losses ,[object Object],[object Object]
Practical Estimate ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Parametric Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Non-parametric Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
TreeNet Process ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Benefits of TreeNet ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],TN Successes
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Key Controls
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Interpreting TN Models
Example: Boston Housing ,[object Object],[object Object],Variable Score LSTAT 100.00 |||||||||||||||||||||||||||||||||||||||||| RM 83.71 ||||||||||||||||||||||||||||||||||| DIS 45.45 ||||||||||||||||||| CRIM 31.91 ||||||||||||| NOX 30.69 |||||||||||| AGE 28.62 ||||||||||| PT 22.81 ||||||||| TAX 19.74 ||||||| INDUS 12.19 |||| CHAS 11.93 ||||
References ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Tree net and_randomforests_2009

  • 1. Introduction to Random Forests and Stochastic Gradient Boosting Dan Steinberg Mykhaylo Golovnya [email_address] August, 2009
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Notas del editor

  1. January 9, 2012
  2. January 9, 2012