Overfitting vs Underfitting#shorts#machinelearning #artificialintelligence

Overfitting and underfitting are common issues in machine learning that affect a model's performance:

Overfitting:

Happens when a model learns not just the underlying pattern in the training data but also the noise and details specific to that data.
As a result, the model performs well on the training data but poorly on unseen data (low generalization).
Example: A model memorizing specific examples instead of learning general trends.
Underfitting:

Occurs when a model is too simple to capture the underlying structure of the data.
The model performs poorly on both the training and testing datasets.
Example: A linear model trying to fit data with a complex, nonlinear relationship.
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