Supervised vs unsupervised machine learning. Overfitting and underfitting.

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Supervised learning uses labeled data to train a model, while unsupervised learning uses unlabeled data. Overfitting occurs when a model is too complex and fits the training data too closely, while underfitting occurs when a model is too simple and fails to capture the underlying patterns in the data.

Supervised learning requires labeled data for training, such as classification or regression tasks.
Unsupervised learning does not require labeled data and is used for clustering or dimensionality reduction tasks.
Overfitting occurs when a model performs well on the training data but poorly on new, unseen data.
Underfitting occurs when a model is too simple to capture the underlying patterns in the data.
Examples of overfitting include a decision tree with too many branches, while examples of underfitting include a linear regression model that cannot capture the true relationship between variables.

Supervised vs unsupervised machine learning. Overfitting and underfitting.

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