Machine Learning Random Forest with Python from Scratch - Overfitting and Underfitting

The video tutorial discusses the concepts of overfitting and underfitting in data modeling. Overfitting occurs when a model performs well on training data but poorly on testing data due to excessive flexibility. Underfitting happens when a model is too rigid, failing to perform well on both training and testing data. The tutorial emphasizes the importance of finding a balance between these two extremes to create an effective model. It also provides real-life examples to illustrate these concepts and hints at practical implementation in future sessions.

What new insight or understanding did you gain from this video?