Model parameters are internal variables that a machine learning model adjusts during training. These values guide how the model transforms input data into predictions. A well-tuned set of parameters helps improve model accuracy, while poor parameter values can lead to underfitting or overfitting.
Key Characteristics of Model Parameters in Machine Learning
Model parameters differ from hyperparameters because they are learned automatically during training. They define the internal logic of the model and determine how input features are processed. Common examples include weights in neural networks, coefficients in linear regression, and attention values in transformers.
Parameters are adjusted iteratively using optimization algorithms like gradient descent. This process minimizes the loss function, allowing the model to learn patterns in the training data. As training progresses, parameters evolve to reduce errors and improve performance.
Examples by Model Type
Linear Regression: Uses coefficients to map input features to output predictions.
Neural Networks: Contain weights and biases in each layer to model complex relationships.
Decision Trees: Use thresholds and rules to split data, though not always considered parameters in the traditional sense.
Transformers / LLMs: Involve millions or billions of internal values such as attention weights and projection matrices.
Why Model Parameters Matter
Model parameters serve as the core knowledge representation within trained AI systems. By adjusting these values, models can adapt to different data distributions and improve generalization. Understanding how parameters work helps with debugging, model tuning, and building robust machine learning solutions.
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