https://github.com/pavansomisetty21/hyper-parameter-tuning
In this we implement hyper parameter tuning
https://github.com/pavansomisetty21/hyper-parameter-tuning
Last synced: 6 months ago
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In this we implement hyper parameter tuning
- Host: GitHub
- URL: https://github.com/pavansomisetty21/hyper-parameter-tuning
- Owner: Pavansomisetty21
- License: apache-2.0
- Created: 2025-03-20T10:18:38.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2025-03-20T10:27:22.000Z (7 months ago)
- Last Synced: 2025-03-20T11:28:56.750Z (7 months ago)
- Language: Jupyter Notebook
- Size: 16.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
## Hyper parameters
Parameters which define the model architecture are referred to as Hyper-parameters and thus this process of searching for the ideal model architecture is referred to as Hyperparameter Tuning.
Two types of parameters exist in machine learning models: one that can be initialized and updated through the data learning process (e.g. the weights of neurons in neural networks, number of epochs, activation function), named Model-parameters; while other named Hyper-parameters, cannot be directly estimated from data learning and must be set before training a Machine learning model because they define the architecture of a machine learning model.
To fit a machine learning model into different problems, its hyper-parameters must be tuned. Selecting the best hyper-parameter configuration for machine learning models has a direct impact on the model’s performance i.e on accuracy and efficiency while training the model. Therefore it needed to be set accurately to get better and efficient results.