Create a computer vision system using decision tree algorithms to solve a real-world problem : Practical Example - Vehic

The video tutorial covers practical classification examples related to self-driving cars, focusing on building a simple neuron model in Python. It explains a practical example involving a truck's speed based on bump height and distance. The tutorial also delves into the confusion matrix, highlighting the importance of understanding error types, particularly type 1 and type 2 errors, in model evaluation.

How does the model determine the speed of the truck based on the bump's height?

What are Type 1 and Type 2 errors in the context of the neural network predictions?

What steps are taken to avoid Type 2 errors in the model?