what is overfitting and underfitting and related solution?

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Overfitting and underfitting are common problems in machine learning where the model either learns the noise in the training data or fails to capture the underlying patterns.

Overfitting occurs when a model learns the training data too well, including the noise and outliers, leading to poor generalization on new data.
Underfitting happens when a model is too simple to capture the underlying patterns in the data, resulting in high bias and low variance.
Solutions to overfitting include using more training data, reducing the complexity of the model (e.g., feature selection, regularization), or using techniques like cross-validation.
To address underfitting, one can increase the model complexity, add more features, or try different algorithms that can capture the data's complexity better.

what is overfitting and underfitting and related solution?

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