xgboost-predictor | Pure Java implementation of XGBoost predictor | Machine Learning library

by h2oai Java Version: 0.3.20 License: Apache-2.0

X-Ray Key Features Code Snippets(1)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | xgboost-predictor Summary

xgboost-predictor is a Java library typically used in Artificial Intelligence, Machine Learning applications. xgboost-predictor has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub, Maven.

Pure Java implementation of [XGBoost] predictor for online prediction tasks.

Support

Quality

Security

License

Reuse

Support

xgboost-predictor has a low active ecosystem.

It has 32 star(s) with 19 fork(s). There are 94 watchers for this library.

It had no major release in the last 12 months.

There are 3 open issues and 1 have been closed. On average issues are closed in 405 days. There are 3 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of xgboost-predictor is 0.3.20

Quality

xgboost-predictor has no bugs reported.

Security

xgboost-predictor has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

xgboost-predictor is licensed under the Apache-2.0 License. This license is Permissive.

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

Reuse

xgboost-predictor releases are available to install and integrate.

Deployable package is available in Maven.

Build file is available. You can build the component from source.

Installation instructions are not available. Examples and code snippets are available.

Top functions reviewed by kandi - BETA

kandi has reviewed xgboost-predictor and discovered the below as its top functions. This is intended to give you an instant insight into xgboost-predictor implemented functionality, and help decide if they suit your requirements.

Read the parameter from the model
Read UTF - 8 string
Read number of bytes
Loads the model
Fills the internal buffer
Read a float array
Predict the given feature
Returns the predicted value for the specified feature group
Returns predictions for the specified feature
Return the index of the leaf nodes of the specified feature
Initialize the gbm object
Compute the weight for a given feature group
Read number of bytes from the stream
Return the leaf path
Use FastMath with FastMath
Gets the leaf index
Gets the leaf path

Get all kandi verified functions for this library.

xgboost-predictor Key Features

No Key Features are available at this moment for xgboost-predictor.

xgboost-predictor Examples and Code Snippets

Getting started,Using Predictor in Java

Java

Lines of Code : 41

License : Permissive (Apache-2.0)

Copy

package biz.k11i.xgboost.demo;

import biz.k11i.xgboost.Predictor;
import biz.k11i.xgboost.util.FVec;

public class HowToUseXgboostPredictor {
    public static void main(String[] args) throws java.io.IOException {
        // If you want to use faste

Community Discussions

Trending Discussions on xgboost-predictor

Is H2O MOJO threadsafe? - xgboost

QUESTION

Is H2O MOJO threadsafe? - xgboost

Asked 2019-Oct-07 at 18:25

This is very similar to H2O MOJO thread safe? but for xgboost.

h2o document Productionizing H2O does not seem to mention anything about it. Is it thread safe to do this in a thread without a lock?

...

ANSWER

Answered 2019-Oct-07 at 18:25

Yes, XGBoost MOJO and XGBoost Java predictor are both thread-safe.

Source https://stackoverflow.com/questions/58253197

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

Vulnerabilities

No vulnerabilities reported

Install xgboost-predictor

You can download it from GitHub, Maven.
You can use xgboost-predictor like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the xgboost-predictor component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .