Nama : Sultan Chairul
NIM : 2581484737
Assigment 3
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What is the key difference between supervised and unsupervised learning? Provide an example for each.
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What is “overfitting” in a machine learning model, and why is it a problem?
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Describe the three main components of reinforcement learning: agent, environment, and reward.
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How does a training dataset differ from a testing dataset, and why is this separation important?
Answer :
1. Supervised vs. Unsupervised Learning Supervised learning uses labeled data, where the correct answers are already provided. For example, an AI learns to identify spam emails using emails labeled as spam or not spam. Unsupervised learning uses unlabeled data to discover patterns, such as grouping customers based on their shopping habits.
2. Overfitting Overfitting occurs when a machine learning model learns the training data too closely, including noise and unnecessary details. As a result, it performs well on training data but poorly on new, unseen data. This makes the model less reliable in real-world situations.
3. Three Main Components of Reinforcement Learning
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Agent: The system that learns and makes decisions.
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Environment: The surroundings in which the agent operates.
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Reward: Feedback that tells the agent whether its actions are good or bad.
For example, in a game, the AI acts as the agent, the game is the environment, and points earned serve as rewards.
4. Training Dataset vs. Testing Dataset A training dataset is used to teach a model to recognize patterns, while a testing dataset is used to evaluate its performance on data it has not seen before. Separating them helps determine whether the model can generalize to new situations rather than simply memorize the training data.
Keterangan : 100%
Bukti : Sudah mengerjakan tugas dengan baik dan benar.
Terimakasih