Short Answer 
The process of analyzing a traffic light image begins with identifying the problem type, which involves classifying the signals as Red, Green, or Amber. Next, categories are defined for each signal, and finally, a classification algorithm is implemented to correctly determine the represented signal in the image using machine learning techniques.
Step 1: Identify the Problem Type
To analyze the traffic light image effectively, the initial step is to categorize the type of problem at hand. In this case, it is essential to recognize that the task is based on classifying the traffic light signals. This classification involves determining which specific signal is represented in the image, ranging from Red to Green to Amber.
Step 2: Define the Categories
Once the problem type is identified, the next step is to clearly define the categories. In the context of traffic light signals, the categories must include:
- Red – signaling to stop
- Green – indicating to go
- Amber – cautioning to prepare to stop
These discrete categories serve as the basis for the classification process, allowing the model to determine which signal is present in any given image.
Step 3: Classify the Image
After establishing the problem type and defining the categories, the final step is to implement the classification algorithm to analyze the image. This typically involves using machine learning techniques that can distinguish between the categories by evaluating the features of the image. The output of this classification will indicate which traffic light color is present, thus solving the problem effectively.