Hyperparameters vs. Parameters


What are Hyperparameters?

A hyperparameter is a entities of a learning algorithm, usually (but not always) having a finite numerical value. That value affects the way the algorithm works. Hyperparameters are not learned by the algorithm itself from data but we set them. They have to be set by the data analyst before running the algorithm.

For example, value of ‘K’ in K-NN algorithm, number of hidden neurons in hidden layer of neural network, filter or kernel size in Convolutional neural network, etc.

What are parameters?

Parameters are variables that define the model, learned by the learning algorithm. Parameters are directly modified by the learning algorithm based on the training data. The goal of learning is to find such values of parameters that make the model optimal in a certain sense.

For example, weights ‘w’ and bias ‘b’ in linear regression and neural networks.

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