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In a classification problem, what does the term 'overfitting' refer to?
In a classification problem, what does the term 'overfitting' refer to?
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In a classification problem, what does the term 'overfitting' refer to?
The model performs well on training data but poorly on unseen data
The model is too simple to capture the underlying trend
The model has too few features
The model is trained on too much data
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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: In a classification problem, what does the term 'overfitting' refer to?
Solution:
Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize to new data.
Steps: 5
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Step 1: Understand that a classification problem involves teaching a model to categorize data into different classes or labels.
Step 2: Know that a model learns from training data, which is the data we use to teach it.
Step 3: Realize that overfitting happens when the model learns the training data too well, including all the details and noise.
Step 4: Recognize that because of overfitting, the model may perform very well on the training data but poorly on new, unseen data.
Step 5: Conclude that overfitting means the model is too specialized to the training data and cannot generalize to other data.
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