What is the difference between a model parameter and a hyperparameter?

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Both control how a model behaves, but they're set at different times. Model Parameters — values learned by the model from data during training . The model updates them via gradient descent. Examples: weights and biases of a neural network, coefficients of linear regression. Hyperparameters — values set by the engineer before training . They control the learning process itself. Examples: learning rate, number of epochs, number of trees in Random Forest, K in KNN. How they're chosen: parameters are learned ; hyperparameters are tuned (Grid Search, Random Search, Bayesian Optimization). A simple but easily confused distinction. Use weights vs learning rate as your canonical pair: weights are learned during training (parameters); learning rate is set before training (hyperparameter).