and features of the training examples, the learning algorithm can identify commonalities and differences among them in order to create a model that can accurately classify new, unseen instances. The goal of concept learning is to generalize from the training examples and make accurate predictions about the class or category of new examples.
Once the learning algorithm has generated a model based on the training examples, it can then be tested on a separate set of validation examples to evaluate its performance and accuracy. This process helps to ensure that the model is able to generalize well to new instances and effectively classify them into the correct categories.
In summary, the concept learning task involves the use of training examples to derive general rules or patterns that can be used to accurately classify new instances. By starting with labeled training data and iteratively refining the model through testing and validation, concept learning algorithms can effectively learn and generalize patterns from data.
