glmtree | Logistic regression trees : a decision tree | Machine Learning library

by adimajo R Version: Current License: No License

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kandi X-RAY | glmtree Summary

glmtree is a R library typically used in Artificial Intelligence, Machine Learning applications. glmtree has no bugs, it has no vulnerabilities and it has low support. You can download it from GitHub.

The goal of glmtree is to build decision trees with logistic regressions at their leaves, so that the resulting model mixes non parametric VS parametric and stepwise VS linear approaches to have the best predictive results, yet maintaining interpretability. This is the implementation of glmtree as described in Formalization and study of statistical problems in Credit Scoring, Ehrhardt A. (see manuscript or web article).

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glmtree has a low active ecosystem.

It has 5 star(s) with 2 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

glmtree has no issues reported. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of glmtree is current.

Quality

glmtree has 0 bugs and 0 code smells.

Security

glmtree has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

glmtree code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

glmtree does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

glmtree releases are not available. You will need to build from source code and install.

Installation instructions, examples and code snippets are available.

It has 2751 lines of code, 0 functions and 18 files.

It has low code complexity. Code complexity directly impacts maintainability of the code.

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glmtree Key Features

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glmtree Examples and Code Snippets

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Community Discussions

Trending Discussions on glmtree

Represent more than 20 levels in a glmtree

Recursive partitioning for factors/characters problem

Using terminal panels in plot() after glmtree()

QUESTION

Represent more than 20 levels in a glmtree

Asked 2021-Feb-16 at 01:45

Currently I am working with the glmtree() function in R. I have some factor variables with 20+ levels. The problem comes with the representation of the tree. There is some information at certain leafs that is impossible to visualise due to the large amount of levels in certain variables (i.e. i_mode has 29 levels).

One possible solution would be to "dummify" those levels. However, I'd rather not do it, if possible at all.

Do you know a method in which I can represent the same plot in a more readable form?

Any clue?

Thank you

...

ANSWER

Answered 2021-Feb-16 at 01:45

My feeling is that it will be challenging to understand such a plot, also beyond the labeling issue. Personally, I would try to break down such a factor into more intelligible groups with fewer levels (not necessarily binary, though).

Having said that, the panel function edge_simple() that draws the edge labels in the tree has some arguments that can help improve the readability, e.g., you can alternate their position and change the font size. For a worked example see: R partykit::ctree offset labels on edges Additionally you could try abbreviating the factor levels prior to learning the tree. However, with 29 levels all of this will probably not help much, I'm afraid.

Source https://stackoverflow.com/questions/66213771

QUESTION

Recursive partitioning for factors/characters problem

Asked 2021-Feb-14 at 09:49

Currently I am working with the dataset predictions. In this data I have converted clear character type variables into factors because I think factors work better than characters for glmtree() code (tell me if I am wrong with this):

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ANSWER

Answered 2021-Feb-14 at 09:49

You are right that glmtree() and the underlying mob() function expect the split variables to be factors in case of nominal information. However, computationally this is only feasible for factors that have either a limited number of levels because the algorithm will try all possible partitions of the number of levels into two groups. Thus, for your i_mode factor this necessitates going through nl levels and mi splits into two groups with:

Source https://stackoverflow.com/questions/66171468

QUESTION

Using terminal panels in plot() after glmtree()

Asked 2021-Jan-24 at 16:38

Using the recursive partitioning model for logit-trees in the following manner

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ANSWER

Answered 2021-Jan-24 at 16:38

In such a situation I recommend to plot the tree on a device that is big enough to show everything and where you can zoom easily etc. For example, one can plot into a big PDF file and then browse and zoom with the PDF viewer. Something like this should work ok:

Source https://stackoverflow.com/questions/65771773

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install glmtree

You can install the development version of glmtree from Github with:.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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