The optimization step is the iteration process of finding the best set of parameters or weights for a machine learning model to predict the outputs based on the inputs accurately. The process involves adjusting the model parameters to minimize a loss function, which measures the difference between the predicted outputs and the actual outputs. The goal is to find the set of parameters that minimizes the loss function and results in the most accurate predictions.

Several optimization algorithms can be used for this purpose, including gradient, stochastic and gradient descent. The optimization algorithm depends on the specific requirements of the machine learning problem and the type of model being used. The optimization step is a key part of the machine learning life cycle, as it determines the best parameters for the model to achieve good performance and accuracy.