Conclusions

In summary, decision trees are a powerful tool in the world of machine learning. Their effectiveness with numeric data lies in their ability to iteratively partition data into subsets based on feature values, ultimately making decisions rooted in the resultant tree structure. Think of them as a roadmap: they divide information into smaller chunks, making decisions at each step based on what they've learned. One big advantage is that they're easy to understand, making them popular in many industries. 

But in this specific situation, decision trees didn't perform as well as hoped. The features used, like latitude, longitude, and temperature, didn't give accurate predictions about flight delays. There are a couple of reasons why this might be. 

1. First, decision trees struggle with really complex relationships between data points, like how temperature and location affect flight delays.

2. Second, the model might need more information, like other weather details, to make better predictions. 

So, while decision trees are useful in many cases, they weren't the best choice here. However, this doesn't mean they're bad overall. Trying out different classification models might lead to better results.