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Supervised learning is a type of machine learning where the model is trained on labeled data to make predictions or decisions.
Uses labeled training data to learn the mapping between input and output variables
The model is trained on a dataset where the correct output is known
Examples include classification and regression tasks
Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern.
Overfitting happens when a model is too complex and captures noise in the training data.
It leads to poor generalization on new, unseen data.
Techniques to prevent overfitting include cross-validation, regularization, and early stopping.
Example: A decision tree with too many branches that perfectly fits the training d
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