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| Pros | Cons | |
|---|---|---|
| Easy to interpret and understand | sensitive to input data range and requires normalization/standardization | |
| Does not assume distribution of classes | Fits linear boundaries | |
| Can be extended to multi-classes | Requires no multicollinearity amongst input features | |
| Runs quickly | Has a linear decision surface; cannot be used for non-linear problems | |
| Model coefficients can be used as indicators of feature importance | Cannot be used to understand complex relationships between variables | |
| Overfitting occurs if number of features is smaller than dataset size | Overfitting can be minimized via regularization of the C parameter |
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