How to provide parameters to machine learning model objects?

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There are several ways to provide parameters to machine learning model objects:

1. Constructor: Many machine learning models have a constructor that takes in parameters as arguments. For example, in scikit-learn, the LinearRegression model has a constructor that accepts several parameters such as fitintercept, normalize, and copyX. You can pass these parameters to the model when creating an instance of the object.

2. Setter methods: Some machine learning models have setter methods that allow you to set specific parameters after the object has been created. For example, in TensorFlow, you can set the learning rate of a neural network model using the learning_rate setter method.

3. Config files: Some machine learning frameworks allow you to specify model parameters in configuration files. For example, in TensorFlow, you can define a YAML file that contains hyperparameters such as learning rate, batch size, and number of epochs, and then pass the file name to the model builder.

4. Command-line arguments: You can also provide model parameters as command-line arguments when running your code. This is useful when you want to experiment with different values of model parameters without changing your code.

5. Environment variables: Some machine learning frameworks allow you to set environment variables to specify model parameters. For example, in PyTorch, you can set the CUDAVISIBLEDEVICES environment variable to specify the GPU to use for training.