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What is overfitting in machine learning?
What is overfitting in machine learning?
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Practice Questions
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Q1
What is overfitting in machine learning?
When a model performs well on training data but poorly on unseen data
When a model is too simple to capture the underlying trend
When a model is trained on too little data
When a model has too many features
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Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize to new data.
Questions & Step-by-step Solutions
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Q
Q: What is overfitting in machine learning?
Solution:
Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize to new data.
Steps: 7
Show Steps
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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