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What does overfitting refer to in machine learning?
What does overfitting refer to in machine learning?
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What does overfitting refer to in machine learning?
A model that performs well on training data but poorly on unseen data
A model that generalizes well to new data
A model that is too simple for the data
A model that has too few features
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Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize.
Questions & Step-by-step Solutions
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Q: What does overfitting refer to in machine learning?
Solution:
Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize.
Steps: 5
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Step 1: Understand that in machine learning, we train models using data called 'training data'.
Step 2: A model is supposed to learn patterns from this training data to make predictions.
Step 3: Overfitting happens when the model learns the training data too well, including its mistakes and random noise.
Step 4: When a model is overfitted, it performs very well on the training data but poorly on new, unseen data.
Step 5: This means the model cannot generalize its learning to make accurate predictions outside of the training data.
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