What is overfitting in machine learning?
Correct Answer: Overfitting is when a model learns the training data too well.
- Step 1: Understand that machine learning models learn from data.
- Step 2: Know that training data is the data used to teach the model.
- Step 3: Realize that a model should learn patterns from the training data.
- Step 4: Overfitting happens when the model learns the training data too well.
- Step 5: This means the model memorizes the data, including any mistakes or noise.
- Step 6: When the model is overfitted, it performs well on training data but poorly on new, unseen data.
- Step 7: The goal is to create a model that generalizes well to new data, not just the training data.
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