Module Question 3
What is the key difference between supervised and unsupervised learning? Provide an example for each.
Answer:
Supervised learning is machine learning using data that already has labels or answers. The model learns from examples with known results so that
What is “overfitting” in a machine learning model, and why is it a problem?
Answer:
Overfitting occurs when a model focuses too much on learning the training data to the point that it seems to “memorize” the data, rather than truly understanding its patterns.
Describe the three main components of reinforcement learning: agent, environment, and reward.
Answers:
An agent is an entity that makes decisions or performs actions; examples include robots or AI.
The environment is the setting in which the agent performs actions and receives responses.
A reward is a value or form of feedback provided after the agent performs an action. Rewards can be positive if the action is correct or aligns with the goal, and negative if the action is suboptimal.
For instance, in a video game, the AI acts as the agent, the game serves as the environment, and the points earned after an action constitute the reward. The AI learns from these rewards to determine better actions.
How does a training dataset differ from a testing dataset, and why is this separation important?
Answers:
A training dataset is data used to train a model so that it can learn patterns from that data. In contrast, a testing dataset is data used to evaluate the model’s performance after the training process is complete. The two must be kept separate to determine whether the model can truly function on new data, rather than simply memorizing the data used for training. This allows for a more objective assessment of the model’s capabilities.
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