1 dépôt
Awesome GitHub RepositoriesTraining Set Size Analysis
Visualizations that plot model accuracy against the amount of training data used.
Distinct from Visual Model Evaluators: Focuses specifically on the relationship between data volume and performance, whereas Visual Model Evaluators is a broader category for general metric plots.
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Awesome Training Set Size Analysis GitHub Repositories
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- rasbt/python-machine-learning-book
rasbt/python-machine-learning-book
12,614Voir sur GitHubThis 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
