There are several ways to evaluate the performance of a machine learning model. One of the most common methods is to use a test set to measure the accuracy of the model. This involves splitting the dataset into a training set and a test set, and then using the training set to train the model and the test set to evaluate its performance. For example, if we are building a classification model to predict the type of flower based on its characteristics, we can split the dataset into a training set and a test set. We can then use the training set to train the model, and the test set to evaluate its performance by calculating the accuracy of the model’s predictions.