3 Repos
Awesome GitHub RepositoriesVisual Model Evaluators
Tools for visually assessing and comparing the performance metrics of trained machine learning models through interactive plots and cross-validation results.
Distinct from Machine Learning Evaluation: Distinct from Machine Learning Evaluation: focuses on the visual, interactive evaluation interface rather than programmatic metric computation.
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Awesome Visual Model Evaluators GitHub Repositories
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- rasbt/python-machine-learning-book
rasbt/python-machine-learning-book
12,614Auf GitHub ansehenThis project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem. The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ
Plots learning curves of accuracies against training set size to determine if more data improves performance.
Jupyter Notebook - biolab/orange3
biolab/orange3
5,635Auf GitHub ansehenOrange3 is a visual data mining platform that provides an interactive canvas for building data analysis workflows without writing code. At its core, it offers a widget-based visual programming environment where users connect configurable components to perform data preprocessing, machine learning model training, statistical evaluation, and interactive visualization. The platform is built on NumPy-backed data tables with domain descriptors that define variable names, types, and roles, and includes a lazy SQL query proxy for working with database tables without loading all data into memory. The
Ships a visual workbench with interactive model evaluation tools, performance curves, and cross-validation results.
Python - salesforce/merlion
salesforce/Merlion
4,476Auf GitHub ansehenMerlion ist ein Machine-Learning-Framework für Zeitreihen, das für Anomalieerkennung und Prognosen entwickelt wurde. Es bietet eine einheitliche Schnittstelle für die Implementierung und Anwendung verschiedener statistischer und Machine-Learning-Modelle auf zeitliche Datenströme. Das Projekt enthält ein Benchmarking-Dashboard, das die visuelle Prüfung und Evaluierung von Modellen anhand historischer Ground-Truth-Datensätze ermöglicht. Diese Weboberfläche erlaubt das Experimentieren mit verschiedenen Modellen auf benutzerdefinierten Datensätzen ohne manuelles Programmieren. Das Framework deckt Funktionen zur Identifizierung von Ausreißern, zur Vorhersage zukünftiger Zeitreihenwerte und zur Messung der Modellgenauigkeit durch simulationsbasiertes Backtesting von produktiven Trainingszyklen ab.
Allows users to visually assess and compare the performance of various models on custom datasets via a dashboard.
Pythonanomaly-detectionautomlbenchmarking
