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Overfitting occurs when a model is too simple to capture the underlying patterns, while underfitting occurs when a model is too complex.
Overfitting occurs when a model fits noise in the training data too well, while underfitting occurs when a model fails to capture the underlying trend.
Both overfitting and underfitting refer to the same phenomenon where models perform poorly.
Underfitting is desirable as it leads to more generalizable models, while overfitting is always a bad practice.
Understanding the Answer
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Overfitting means the model learns the noise in the training data, so it works well on that data but poorly on new data. Other options are incorrect because This option mixes up the two concepts; It says both problems are the same, but they are opposite.
Key Concepts
Loss Functions
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Deep Dive: Loss Functions
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Definition
Loss functions quantify how well a predictor approximates the true output values. They are used to measure the discrepancy between predicted and actual values. Common examples include quadratic loss functions that penalize the squared differences.
Topic Definition
Loss functions quantify how well a predictor approximates the true output values. They are used to measure the discrepancy between predicted and actual values. Common examples include quadratic loss functions that penalize the squared differences.
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