모델 (기계학습)
In general, any mathematical construct that processes input data and returns output. Phrased differently, a model is the set of parameters and structure needed for a system to make predictions.1
In supervised machine learning, a model takes an example as input and infers a prediction as output. Within supervised machine learning, models differ somewhat. For example:
- A linear regression model consists of a set of weights and a bias.
- A neural network model consists of:
- A set of hidden layers, each containing one or more neurons.
- The weights and bias associated with each neuron.
- A decision tree model consists of:
- The shape of the tree; that is, the pattern in which the conditions and leaves are connected.
- The conditions and leaves.
You can save, restore, or make copies of a model.
Unsupervised machine learning also generates models, typically a function that can map an input example to the most appropriate cluster.