Module Question 3-BD107-Amelia Nurul Mupty-2581499006

Module Question 3

1. What is the key difference between supervised and unsupervised learning? Provide an example for each.
2. What is “overfitting” in a machine learning model, and why is it a problem?
3. Describe the three main components of reinforcement learning: agent, environment, and reward.
4. How does a training dataset differ from a testing dataset, and why is this separation important?
   the answer
   1. Supervised vs. Unsupervised Learning:
      Supervised learning uses labeled data to make predictions. Example: predicting house prices. Unsupervised learning uses unlabeled data to find patterns. Example: grouping customers by shopping behavior.
   2. Overfitting:
      Overfitting happens when a model learns the training data too closely, including its noise. It becomes less accurate when given new data.
   3. Reinforcement Learning:
      • Agent: The learner that makes decisions.
      • Environment: The world where the agent acts.
      • Reward: Feedback that tells the agent whether its action was good or bad.
   4. Training vs. Testing Dataset:
      A training dataset is used to teach the model, while a testing dataset is used to evaluate its performance on new data. This separation helps check whether the model can generalize well.