xlearn | High performance factorization machines for Ruby | Recommender System library

by ankane Ruby Version: Current License: Apache-2.0

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | xlearn Summary

xlearn is a Ruby library typically used in Artificial Intelligence, Recommender System applications. xlearn has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

xLearn - the high performance machine learning library - for Ruby.

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Support

xlearn has a low active ecosystem.

It has 44 star(s) with 2 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 0 open issues and 1 have been closed. On average issues are closed in 165 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of xlearn is current.

Quality

xlearn has 0 bugs and 0 code smells.

Security

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

xlearn code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

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.

Reuse

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

Installation instructions, examples and code snippets are available.

xlearn saves you 190 person hours of effort in developing the same functionality from scratch.

It has 469 lines of code, 47 functions and 13 files.

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

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xlearn Key Features

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xlearn Examples and Code Snippets

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Community Discussions

Trending Discussions on xlearn

xlearn predictions error give a different mse than output by the function

Custom ML function not working: undefined columns selected

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

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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.

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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

Add this line to your application’s Gemfile:.
Use XLearn::FM for factorization machines and XLearn::FFM for field-aware factorization machines. Save the model to a file. Load the model from a file. Save a text version of the model. Pass a validation set. Get the bias term, linear term, and latent factors.