MLBox
ML.NET-Auto-ML-ESRB-Rating-Classification
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| MLBox | ML.NET-Auto-ML-ESRB-Rating-Classification | |
|---|---|---|
| 1 | 3 | |
| 1,475 | 3 | |
| - | - | |
| 0.0 | 0.0 | |
| 9 months ago | about 2 years ago | |
| Python | C# | |
| GNU General Public License v3.0 or later | - |
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MLBox
ML.NET-Auto-ML-ESRB-Rating-Classification
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The 3 Core Machine Learning Tasks
This trained model would then be able to predict ESRB ratings for video games that had yet to be released and generate some degree of probability that a game might be in any given rating.
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What can you do in ML.NET with C#?
For more code examples around multi-class classification, take a look at my article and video on using AutoML and ML.NET to predict ESRB ratings for video games which also serves as an in-depth introduction to ML.NET for a single task.
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Using C# and Auto ML in ML .NET to Predict Video Game Ratings
This article comes with an accompanying GitHub repository containing a .NET 6 console app that can train itself to evaluate video games and predict an ESRB rating for games the trained model hasn't seen before.
What are some alternatives?
lazypredict - Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning
machinelearning-samples - Samples for ML.NET, an open source and cross-platform machine learning framework for .NET.
AlphaPy - Python AutoML for Trading Systems and Sports Betting
NsfwSpy.NET - A .NET image and video classifier used to identify explicit/pornographic content written in C#.
nylon - An intelligent, flexible grammar of machine learning.
pycaret - An open-source, low-code machine learning library in Python
draf - Demand Response Analysis Framework (DRAF)
SentimentAnalyzer - This repo contains the source code of SentimentAnalyzer. It's an on-device (offline) open-source library to find out what customers think of your brand or topic by analyzing raw text for clues about positive or negative sentiment. Powered by ML.NET
gmr - Gaussian Mixture Regression
deep-kernel-transfer - Official pytorch implementation of the paper "Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels" (NeurIPS 2020)
Towards-Explainable-AI-System-for-Traffic-Sign-Recognition-and-Deployment-in-a-Simulated-Environment - This project is part of the CS course 'Systems Engineering Meets Life Sciences I' at Goethe University Frankfurt. In this Computer Vision project, we present our first attempt at tackling the problem of traffic sign recognition using a systems engineering approach.