Subscribe to Vincent Granville's Weekly Digest:

Improve accuracy of regression tree

Hi,

I'm working on some analysis using regression tree. But the accuracy level of the tree is coming low. Can I suggest some methods which will improve the accuracy of regression tree & able to represent the splitting logic by building tree?

I tried with Random forest. It is increasing the accuracy but I'm not able to represent my final outcome as a tree.

Thanks in advance

Views: 258

▶ Reply to This

Replies to This Discussion

Permalink Reply by Alok Shende on October 3, 2012 at 12:33am

Boosted additive models is the state of the art technique when it comes to classifications trees. The other benefit is that boosted additive models also address nonlinearity of the underlying data very well.

Permalink Reply by Niladri Chakraborty on October 3, 2012 at 2:16am

Actually I need to show the decision logic also,other than improving the accuracy. That's the reason of not using Random forest.Do you think I can show the decision logic using Boosted additive model? Also I need to it for regression tree

Permalink Reply by Alok Shende on October 3, 2012 at 7:15am

The great thing with boosted additive models or gradient boosted models (GBM) as they are called is that they capture the relationship between dependent and independent variable very well. just like you do so in regression. On top they have the accuracy of machine learning algorithm.

The decision logic of regression and accuracy of machine learning - that is GBM.

Permalink Reply by Niladri Chakraborty on October 3, 2012 at 8:02am

But you are not giving answer to my basic question. IS IT CAPABLE TO SHOW THE DECISION LOGIC FOR THE  SPLIT???

Permalink Reply by Alok Shende on October 3, 2012 at 8:23am

Of course, I thought I answered that. You might like to check your software though.

Permalink Reply by Niladri Chakraborty on October 3, 2012 at 8:32am

Can you please suugest me some material on GBM?

Permalink Reply by Alok Shende on October 3, 2012 at 8:37am

Chapter 10, Book on statistical learning by Hastie, Tibshirani and Friedman. It should be available for download.

Permalink Reply by Niladri Chakraborty on October 3, 2012 at 8:40am

Thank you

RSS

Top Content 

1 Why is it so hard to count this way?

Why is it so hard to count this way?
2 Yelp won't hire unemployed data scientists

Yelp won't hire unemployed data scientists
3 Normalized odds-ratio

Normalized odds-ratio
4 Reverse clustering for tactical warfare

Reverse clustering for tactical warfare
5 Time Period for Analytical Positions Recruitment.

Time Period for Analytical Positions Recruitment.
6 Outliers : Capping rule

Outliers : Capping rule
7 Seven Questions on Adopting Analytics Culture

Seven Questions on Adopting Analytics Culture
8 Correlation and R-Squared for Big Data

Correlation and R-Squared for Big Data
9 Cross-validation in R: a do-it-yourself and a black box approach

Cross-validation in R: a do-it-yourself and a black box approach
10 Do You Have What It Takes To Be a Data Scientist?

Do You Have What It Takes To Be a Data Scientist?

Follow us

Big Data Jobs

Manager, Data Scientist

Statistical Modeling Analyst

Data Analyst - AllOut

Data Scientists (all levels), Amazon Consumer Analytics

Lead Data Scientist

Big Data Enterprise Architect - Hadoop

Hadoop Engineer - salesforce.com

Manager, League Operations & Officiating

Sr. Software Engineer - Sony

Director, Office of Planning, Decision Support, and Assessment

© 2013   AnalyticBridge.com is a subsidiary and dedicated channel of Data Science Central LLC

Badges  |  Report an Issue  |  Terms of Service