The term that refers to the process of modifying model parameters to reduce errors on the training data is called training.
Training is an iterative process where the model is presented with training examples and its parameters are adjusted to improve its performance. This is typically done by minimizing a loss function, which measures the difference between the model's predictions and the actual values.
There are many different algorithms for training machine learning models, but they all share the same basic principle of iteratively adjusting the model's parameters to reduce errors. Some of the most common training algorithms include:
- Gradient descent: This is a popular algorithm that uses the gradient of the loss function to update the model's parameters.
- Stochastic gradient descent: This is a variant of gradient descent that updates the model's parameters based on a single training example at a time.
- Momentum: This is a technique that can be used to improve the convergence of gradient descent by incorporating information from past updates.
- Adam: This is a more complex algorithm that combines several techniques to improve the efficiency and stability of training.
The choice of training algorithm depends on a number of factors, such as the type of model being trained, the size of the training data, and the desired level of accuracy.
