In their lecture, the professor outlined Decision Trees, a machine learning algorithm used for classification and regression tasks, notable for its tree-like structure where nodes represent decisions based on input features, and branches show possible outcomes, leading to a final prediction at the leaves. This method is appreciated for its simplicity and versatility with different data types, with applications ranging from medical diagnosis, using patient data to predict diseases, to finance, for assessing creditworthiness based on personal financial data. Key metrics in decision trees include the Gini Index, which measures dataset impurity and aims for lower values for more accurate predictions, and Information Gain, which evaluates a feature’s effectiveness in reducing uncertainty, guiding the algorithm to prioritize features that best classify the dataset.