xlearn | High performance , easy-to-use , and scalable machine learning | Machine Learning library

 by   aksnzhy C++ Version: 0.40a1 License: Apache-2.0

kandi X-RAY | xlearn Summary

kandi X-RAY | xlearn Summary

xlearn is a C++ library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. xlearn has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

xLearn is a high performance, easy-to-use, and scalable machine learning package that contains linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM), all of which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning problems on large-scale sparse data. Many real world datasets deal with high dimensional sparse feature vectors like a recommendation system where the number of categories and users is on the order of millions. In that case, if you are the user of liblinear, libfm, and libffm, now xLearn is your another better choice.
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              xlearn has a medium active ecosystem.
              It has 3038 star(s) with 538 fork(s). There are 108 watchers for this library.
              OutlinedDot
              It had no major release in the last 12 months.
              There are 185 open issues and 114 have been closed. On average issues are closed in 91 days. There are 11 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of xlearn is 0.40a1

            kandi-Quality Quality

              xlearn has no bugs reported.

            kandi-Security Security

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

            kandi-License License

              xlearn 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.

            kandi-Reuse Reuse

              xlearn releases are available to install and integrate.

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            xlearn Key Features

            No Key Features are available at this moment for xlearn.

            xlearn Examples and Code Snippets

            No Code Snippets are available at this moment for xlearn.

            Community Discussions

            QUESTION

            xlearn predictions error give a different mse than output by the function
            Asked 2021-Feb-10 at 18:00

            the xlearn predict function gives a different mse than what you get by looking at the predictions and calculating it yourself. Here is code to do this; you can run it by cloning the xlearn repository and copying the below code in demo/regression/house_price in the repository

            ...

            ANSWER

            Answered 2021-Feb-09 at 23:58

            A lot of people use 1/2 MSE for the loss because it makes the derivative "easier". Given that they use the word "loss" rather than "MSE" or something like that, I'd bet this is what's going on.

            For clarity, if your loss is

            1/2n * [(y_1 - p_1)^2 + ... + (y_n - p_n)^2]

            then the derivative (wrt p) would be

            -1/n * [(y_1 - p_1) + ... + (y_n - p_n)]

            The 2 goes away because you end up multiplying by 2 for the power rule.

            pardon the formatting... I don't know how to do math stuff here.

            Source https://stackoverflow.com/questions/66026339

            QUESTION

            Custom ML function not working: undefined columns selected
            Asked 2018-Jun-18 at 14:36

            I am trying to write a custom function to do logistic regression-based ML with the caTools package, but I keep getting the error: undefined columns selected.

            I checked the input to xlearn and ylearn arguments to the logit_boost function and, as explained in the documentation, they are respectively dataframe containing feature and a vector of labels. So not sure what I am doing wrong.

            ...

            ANSWER

            Answered 2018-Jun-18 at 14:36

            In help(LogitBoost) examples section, Label = iris[, 5] results in a vector, as expected in the ylearn argument to LogitBoost().

            In your code, label_train <- train %>% dplyr::select(.data = ., !!rlang::enquo(x)) results in a data.frame. dplyr, by design, defaults to drop = FALSE (and even ignores the argument) when only one column is selected.

            We could do:

            Source https://stackoverflow.com/questions/50868808

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

            Vulnerabilities

            No vulnerabilities reported

            Install xlearn

            You can download it from GitHub.

            Support

            xLearn has been developed and used by many active community members. Your help is very valuable to make it better for everyone. Note that, please post iusse and contribution in English so that everyone can get help from them.
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            Install
          • PyPI

            pip install xlearn

          • CLONE
          • HTTPS

            https://github.com/aksnzhy/xlearn.git

          • CLI

            gh repo clone aksnzhy/xlearn

          • sshUrl

            git@github.com:aksnzhy/xlearn.git

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