LiYangHart/Hyperparameter-Optimization-of-Machine-Learning-Algorithms
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models (easy&clear)
This project helps data scientists and machine learning engineers fine-tune their machine learning models to achieve better performance. You input your machine learning model and the data you're using, and it provides optimized settings for key parameters that control how the model learns. This results in a more accurate or efficient model for tasks like classification or regression.
1,321 stars. No commits in the last 6 months.
Use this if you are building or deploying machine learning models and want to systematically find the best configuration for algorithms like Random Forest, SVM, KNN, or Neural Networks.
Not ideal if you are looking for a fully automated machine learning solution that handles everything from data preparation to model deployment without requiring hands-on tuning.
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Jupyter Notebook
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MIT
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Last pushed
Sep 22, 2022
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