Search Results for "ranking"

XGBoost is an optimized distributed gradient boosting library, designed to be scalable, flexible, portable and highly efficient. It supports regression, classification, ranking and user defined objectives, and runs on all major operating systems and cloud platforms. XGBoost works by implementing machine learning algorithms under the Gradient Boosting framework. It also offers parallel tree boosting (GBDT, GBRT or GBM) that can quickly and accurately solve many data science problems. XGBoost can be used for Python, Java, Scala, R, C++ and more. ...

Job-Recommend explores the basics of building a job recommendation workflow, from data preparation to ranking simple candidate matches. It treats job postings and résumés as structured items and applies straightforward matching signals such as keywords, skills overlap, or vectorized features. The repository is educational in spirit, focusing on clarity rather than heavy infrastructure or opaque models. You can study how to transform raw text into features and how to evaluate simple heuristics or baseline models. ...

...It is designed as a successor and improvement to older tools like HashID and Hash-Identifier, focusing on up-to-date hash databases and better usability. One of its core ideas is popularity-aware ranking: when you feed in a hash, it prioritizes likely real-world types such as NTLM over obscure ones like Skype hashes, instead of treating them equally. The tool provides concise “hash summaries” that explain where a given hash format is commonly used, helping users decide how to proceed with cracking or further analysis. Name-That-Hash is accessible via a Python CLI (nth) and also exposes an API and JSON output, making it easy to plug into other tools or workflows, and there is also a web app that requires no local installation.

...For users browsing GitHub casually or seeking entertainment rather than strictly utility, StarsAndClown offers a curated feed of repositories that stand out — sometimes for good reason, sometimes for quirky appeal. As a public listing, it helps surface interesting corners of GitHub that mainstream ranking systems may neglect, offering a “pop-culture catalogue” of software rather than purely technical resources.

LibRec is a Java library for recommender systems (Java version 1.7 or higher required). It implements a suit of state-of-the-art recommendation algorithms, aiming to resolve two classic recommendation tasks: rating prediction and item ranking.