$1.$ Rule$-$Based Classification: Using a rule$-$based approach, create rules to classify samples into either the positive or negative class based on the given attributes $($Color$,$ Size, Temperature, Weight$).$

$2.$ Rule Interpretation: Interpret the rules you created in $1).$ Explain the conditions under which a sample is classified as positive or negative.

$3.$ Rule Modification: Modify one of the rules to observe how it affects the classification. Explain the impact of the modification.

$4.$ Rule Confidence: Assign confidence levels to the rules you created in $1).$ For example, how confident are you in classifying a sample as positive based on the rules?

$5.$ Rule Expansion: Add a new rule to your rule$-$based system.

$6.$ Rule Visualization: Visualize your rule$-$based system in a decision tree format. Each rule should correspond to a branch in the decision tree.

$7.$ Rule Application: Apply your rule$-$based system to the following instance:

Color: Green, Size: Small, Temperature: $24\mathrm{.}0,$ Weight: $260$

$8.$ Rule Refinement: Refine one of the rules to make it more specific or general. Explain the reasoning behind the refinement.

$9.$ Rule Generalization: Generalize one of the rules to make it more applicable to a broader set of samples. Explain the reasoning behind the generalization.