How do supervised and unsupervised learning differ?
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Supervised and unsupervised learning are two core types of machine learning that differ in how they use data to train models.
Supervised Learning involves training a model on a labeled dataset, meaning the data includes both input features and corresponding output labels (the target variable). The goal is for the model to learn the mapping between inputs and outputs, enabling it to predict the output for new, unseen data. Examples of supervised learning tasks include classification (e.g., spam email detection) and regression (e.g., predicting house prices). Common algorithms used in supervised learning include linear regression, logistic regression, support vector machines, and neural networks.
Unsupervised Learning, on the other hand, works with unlabeled data, where the model only has input features but no predefined labels or outputs. The goal is to discover hidden patterns or structures in the data, such as grouping similar data points or identifying anomalies. Common tasks in unsupervised learning include clustering (e.g., customer segmentation) and dimensionality reduction (e.g., reducing the number of features in a dataset). Popular algorithms include k-means clustering, hierarchical clustering, and principal component analysis (PCA).
In summary, the main difference is that supervised learning relies on labeled data to make predictions, while unsupervised learning identifies patterns in data without explicit labels.
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