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Overfitting

Overfitting occurs when a machine learning model learns the details and noise in the training data to the extent that it negatively impacts the model’s performance on new, unseen data. The model becomes overly complex and fits the training data too closely, reducing its ability to generalize.

Use Cases

High-Dimensional Data

Models with too many features may overfit if the data is noisy.

Small Datasets

Overfitting is common when training on small datasets with limited diversity.

Complex Models

Deep neural networks and decision trees are prone to overfitting if not regularized.

Importance

Generalization

Prevents the model from making inaccurate predictions on new data.

Model Evaluation

Ensures that the model's performance metrics reflect real-world accuracy.

Bias-Variance Tradeoff

Balances model complexity with its ability to generalize to new data.

Analogies

Overfitting is like memorizing specific answers rather than understanding the concepts in a test. Just as memorization may help with specific questions but fails when faced with new questions, overfitted models perform well on training data but poorly on new, unseen data.

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