Training and testing processes

Machine learning has revolutionized the way we solve complex problems by enabling computers to learn from data and make predictions or decisions without being explicitly programmed. One crucial aspect of machine learning is training models, which involves teaching algorithms to recognize patterns and relationships in data. Two fundamental methods for training machine learning models are model.fit() and model.predict().

The model.fit() function lies at the heart of training a machine learning model. It is the process by which a model learns from a labeled dataset to make accurate predictions. During training, the model adjusts its internal parameters to minimize the discrepancy between its predictions and the true labels in the training data. This iterative optimization process, often referred to as “learning,” allows the model to generalize its knowledge and perform well on unseen data.

In addition to the training data and labels,...