Work with Semantic Versions in Go

Local Interpretable Model-Agnostic Explanations (R port of original Python package)

A input text view for entering pin code.

Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models

Visual text selection

A Minimalistic Framework for Creating, Managing and Training Multiple Caret Models

Set and get Caret position (contenteditable or TextArea) using Vanilla JavaScript

AngularJS directive for caret aware elements

Carrot is a simplistic string-based esolang whose memory is on a tape of tapes

Tailwindcss plugin to color caret in input fields

Dockerfiles for ML with R - built on top of Rocker

Applied Machine Learning

Applied Predictive Modeling with caret

Implementations for Feedzai's OpenML APIs to allow for usage of machine learning models in the R programming language.

Regress using many metalearning packages including caret and TPOT from one script. Also artificial data generator, comparison script and several expiraments. Messy.

Collections of Tutorials and Demos for Learning Julia in Data Science and Computing

jQuery plugin to manipulate the caret's position in a text box

Using Azure Machine Learning studio and R’s Caret package to analyse the trend and understand the underlying influencing factors of Ireland property rental market. Data from Private Residential Tenancies Board (PRTB) rent index were used and built prediction models using regression algorithms.

데이터 과학 모형

Exercises From Book "Applied Predictive Modeling" by "Kuhn and Johnson (2013)"

Caret R Models Deployment using SQL databases

🥊 Library that allows to animate cursor movement between text fields

📚 Notes from a bookclub's weekly exploration of Applied Predictive Modeling

R package for stacking caret models

R exercises (2016)

Large-scale digital mapping of soil organic carbon content by using machine learning algorithms

Create APIs for the deployment of R models with minimal code

Analyzes network traffic and tells whether the query is normal or a type of attack. 3 classifiers are built and tested: Naive Bayes, Decision Trees, Random Forests, Followed by a complete visualization of results