stance_detection | Stance Detection for the Fake News Challenge | Machine Learning library

by ourownstory Python Version: Current License: No License

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kandi X-RAY | stance_detection Summary

stance_detection is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow, Nodejs, Neural Network applications. stance_detection has no bugs, it has no vulnerabilities and it has low support. However stance_detection build file is not available. You can download it from GitHub.

Stance Detection for the Fake News Challenge with Conditional Encoding and Attention LSTM, as Stanford CS224N class project by Stephen Pfohl, Oskar Triebe and Ferdinand Legros.

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stance_detection has a low active ecosystem.

It has 12 star(s) with 7 fork(s). There are 1 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. On average issues are closed in 616 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of stance_detection is current.

Quality

stance_detection has no bugs reported.

Security

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

License

stance_detection 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.

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

stance_detection has no build file. You will be need to create the build yourself to build the component from source.

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stance_detection Key Features

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stance_detection Examples and Code Snippets

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Community Discussions

Trending Discussions on stance_detection

PySpark MLlib: AssertionError: Classifier doesn't extend from HasRawPredictionCol

QUESTION

PySpark MLlib: AssertionError: Classifier doesn't extend from HasRawPredictionCol

Asked 2018-Apr-29 at 23:54

I am a newbie in Spark . I want to use multiclass classification for SVM in PySpark MLlib. I installed Spark 2.3.0 on Windows.

But I searched and found that SVM is implemented for binary classification only in Spark , so we have to use one-vs-all strategy. It gave me an error when I tried to use one-vs-all with SVM . I searched for the error but do not find a solution for it.

I used the code of one-vs-all from this link https://spark.apache.org/docs/2.1.0/ml-classification-regression.html#one-vs-rest-classifier-aka-one-vs-all

here is my code :

...

ANSWER

Answered 2018-Apr-29 at 23:45

You get the error because you are trying to use a model from Spark ML (OneVsRest) with a base binary classifier from Spark MLlib (SVMWithSGD).

Spark MLlib (the old, RDD-based API) and Spark ML (the new, dataframe-based API) are not only different libraries, but they are also incompatible: you cannot mix models between them (looking closer at the examples, you'll see that they import the base classifier from pyspark.ml, and not from pyspark.mllib, as you are trying to do here).

Unfortunately, as at the time of writing (Spark 2.3) Spark ML does not include SVMs, you cannot currently use the algorithm as a base classifier with OneVsRest...

Source https://stackoverflow.com/questions/50091896

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

Vulnerabilities

No vulnerabilities reported

Install stance_detection

You can download it from GitHub.
You can use stance_detection like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

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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