[MINI] Cross Validation

This podcast mini-episode discusses the technique of Cross Validation, which involves splitting a dataset into small partitions, training a model, and validating its predictive power. The hosts explore the importance of models, good fit, and the process of training. They highlight the significance of cross-validation in data science to avoid overfitting and improve predictive power, using examples such as predicting sales data and training a jazz music classifier. Finally, they explain the concept of cross-validation in machine learning and its usefulness for limited or new data.