Displaying 1 to 13 from 13 results

limdu - Machine-learning for Node.js

  •    Javascript

Limdu is a machine-learning framework for Node.js. It supports multi-label classification, online learning, and real-time classification. Therefore, it is especially suited for natural language understanding in dialog systems and chat-bots.Limdu is in an "alpha" state - some parts are working (see this readme), but some parts are missing or not tested. Contributions are welcome.

scoruby - Ruby Scoring API for PMML

  •    Ruby

Ruby scoring API for Predictive Model Markup Language (PMML).Currently supports Decision Tree, Random Forest Naive Bayes and Gradient Boosted Models.

sentimentalizer - Sentiment analysis with Machine Learning

  •    Ruby

Inspired by Sentan node-sentiment. This gem can be used separately or integrated with rails app.

GaussianNB - Gaussian Naive Bayes (GaussianNB) classifier

  •    Python

Simple Gaussian Naive Bayes classifier implementation. It also implements 5-fold cross-validation. Compared performance with Zero-R algorithm.

sux0r - it sux0rs up all the web

  •    PHP

sux0r is a blogging package, an RSS aggregator, a bookmark repository, and a photo publishing platform with a focus on Naive Bayesian categorization and probabilistic content. OpenID enabled (version 1.1); as both a consumer and a provider. Current status: maintanence.

Machine_Learning - Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks

  •    Jupyter

Esse repositório foi criado com a intenção de difundir o ensino de Machine Learning em português. Os algoritmos aqui implementados não são otimizados e foram implementados visando o fácil entendimento. Portanto, não devem ser utilizados para fins de pesquisa ou outros fins além dos especificados.

classifier - A general purpose text classifier

  •    Go

A naive bayes text classifier. There are two methods of classification: io.Reader or string. To classify strings, use the TrainString or ClassifyString functions. To classify larger sources, use the Train and Classify functions that take an io.Reader as input.

whichx - A small, no dependencies, Naive Bayes Text Classifier for JavaScript

  •    Javascript

WhichX is a Naive Bayes' Classifier written in Javascript for classifying short text descriptions into categories. It is a very small library with a very simple API and no dependencies. To see a working demo you can also go to http://www.rudikershaw.com/articles/whichpet. If you are using Node start by requiring whichx.

TextClassificationApp - Building and Deploying A Serverless Text Classification Web App

  •    Jupyter

In this project, over a series of blog posts I'll be buidling a model document classification, also known as text classification and deploying the model as part of a web application to predict the topic of research papers from their abstract. In the first blog post I will be working with the Scikit-learn library and an imbalanced dataset (corpus) that I will create from summaries of papers published on arxiv. The topic of each paper is already labeled as the category therefore alleviating the need for me to label the dataset. The imbalance in the dataset will be caused by the imbalance in the number of samples in each of the categories we are trying to predict. Imbalanced data occurs quite frequently in classification problems and makes developing a good model more challenging. Often times it is too expensive or not possible to get more data on the classes that have to few samples. Developing strategies for dealing with imbalanced data is therefore paramount for creating a good classification model. I will cover some of the basics of dealing with imbalanced data using the Imbalance-Learn library as well as building a Naive Bayes classifier and Support Vector Machine using from Scikit-learn. I will also over the basics of term frequency-inverse document frequency and visualizing it using the Plotly library.

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