OverFitting and underfitting conditions and example

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Accepted Answer

Overfitting and underfitting are common issues 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 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.
Examples of overfitting include a decision tree with too many branches that perfectly fits the training data but performs poorly on test data.
An example of underfitting is a linear regression model that fails to capture the non-linear relationship between variables in the data.

OverFitting and underfitting conditions and example

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