Problem 1 (4 points):
Read Section 20.5 (Neural Networks) of the textbook. In your own words
describe the important differences between neural networks and decision trees as
approaches to learning. For each give an example of a real-world problem that
is suited to that style of learning.
Justify your answers.
The following table outlines the important differences and provides examples:
| Attribute | Decision Trees | Neural Networks |
| Problem solving | Algorithmic | Adaptive |
| Input | Categorical data* | Continuous real numbers |
| Output | Discrete classes with certainty and explanations | Discrete classes |
| Solutions correspond to | FOL conjunctions | Arbitrary mathematical functions |
| Robustness to noise | Limited | Very good |
| Applications | Credit risk analysis | Autonomous driving |
| Marketing (Consumer behavior prediction) | Face recognition |
*Note: Decision Trees can be adapted to deal with continuous real numbers as input, but their capabilities are very limited when working with these kinds of attributes.
Problem 2 (6 points):
For full credit on both assignments your program had to build a decision tree from the following training sets and then classify the corresponding test sets.
Weather Example: Training, Test
The solution to all the examples can be found at the updated page here.