decisiontrees | High performance implementations of gradient boosting | Machine Learning library

by ajtulloch JavaScript Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | decisiontrees Summary

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

Gradient Boosting, Random Forests, etc. implemented in Go. [Bitdeli Badge] "Bitdeli Badge").

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decisiontrees has a low active ecosystem.

It has 60 star(s) with 15 fork(s). There are 5 watchers for this library.

It had no major release in the last 6 months.

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

It has a neutral sentiment in the developer community.

The latest version of decisiontrees is current.

Quality

decisiontrees has no bugs reported.

Security

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

License

decisiontrees is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

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

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decisiontrees Examples and Code Snippets

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Community Discussions

Trending Discussions on decisiontrees

Predict_proba for IsolationForest

Vector in the initializer list of a class

How to correctly provide input data for Accord.NET DecisionTrees

QUESTION

Predict_proba for IsolationForest

Asked 2019-Dec-06 at 12:23

I try to use an isolation Forest for an outlier detection (fraud detection). If I run the code below (with train and test set):

...

ANSWER

Answered 2019-Dec-06 at 12:23

There isn't a predict_proba in this module, because it does not use probabilities to evaluate each sample as an outlier, but instead a score.

Looking at the documentation you provided, each samples is classified using a decision function with the following formula:

Source https://stackoverflow.com/questions/59212581

QUESTION

Vector in the initializer list of a class

Asked 2019-Apr-09 at 02:56

I have a learner list/vector defined in one .hpp file like this:

std::vector learners_;.

Now in the constructor, this is used like this:

...

ANSWER

Answered 2019-Apr-09 at 02:56

dr_(dr), ensembleSize_(ensembleSize), learners_({}) : These are used to initialize the object variables of Bagging Class to the specific values provided when the constructor is called.

({}) means that the std::vector learners_ is initialized to just an empty vector through an initializer list (it's a C++11 thing).

Source https://stackoverflow.com/questions/55584332

QUESTION

How to correctly provide input data for Accord.NET DecisionTrees

Asked 2017-Apr-28 at 13:39

I'm trying to learn machine learning and in particular decision trees, I copied this piece of code from the Accord .Net framework website and it doesn't seem to be working for me, and I can't figure out why. The error it gives me is on line 40 saying:"System.IndexOutOfRangeException: 'Index was outside the bounds of the array.'" I'm not sure what I'm getting wrong, the data set it uses is found here: https://en.wikipedia.org/wiki/Iris_flower_data_set Maybe I'm having trouble giving it the data set in the correct manner? By the way I'm using Visual Studio Community 2017.

This is the code:

...

ANSWER

Answered 2017-Apr-28 at 12:33

Judging by the code sample itself, all you need is a static class containing your data in CSV format:

Source https://stackoverflow.com/questions/43607740

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