What is biasing and what is overfitting and underfitting

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Biasing is the error due to overly simplistic assumptions in the learning algorithm. Overfitting is when a model is too complex and fits the training data too closely, leading to poor generalization. Underfitting is when a model is too simple to capture the underlying structure of the data.

Biasing occurs when a model has high error on both training and test data due to oversimplified assumptions.
Overfitting happens when a model is too complex and captures noise in the training data, leading to poor performance on unseen data.
Underfitting occurs when a model is too simple to capture the underlying patterns in the data, resulting in high error on both training and test data.
Examples: A linear regression model that assumes a linear relationship between variables may exhibit biasing if the true relationship is non-linear. A decision tree with too many branches may overfit the training data. A linear regression model with only one feature may underfit the data.

What is biasing and what is overfitting and underfitting

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