Overfitting Vs Underfitting

In this classroom discussion, the teacher introduces the concepts of overfitting and underfitting in machine learning models. A student explains that overfitting occurs when the model is too complex and fits the training data too closely, resulting in poor generalization to new data. The student uses the analogy of a familiar route to work, where an unexpected traffic jam causes a delay, highlighting the over-reliance on past experiences. The teacher acknowledges the student’s explanation. The student then explains underfitting as a situation where the model is too simple and fails to capture important patterns in the data, leading to poor performance. The student uses the analogy of taking a multiple-choice exam without preparation, where guessing without any information results in mostly incorrect answers. The teacher commends the class for their understanding of the concepts