Model Parameters
In the context of data science and artificial intelligence, model parameters refer to the values that are learned by a machine learning algorithm during the training process. These values are used to make predictions on new data. Model parameters can be adjusted to improve the accuracy of the model. The process of adjusting the model parameters is called model tuning. The number of model parameters can vary depending on the complexity of the model. For example, a linear regression model has a small number of parameters, while a deep neural network can have millions of parameters. Model parameters can be regularized to prevent overfitting, which occurs when a model is too complex and fits the training data too closely, resulting in poor performance on new data. Regularization techniques include L1 and L2 regularization, which add a penalty term to the loss function to encourage the model to have smaller parameter values.
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