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Supervised Learning is a type of machine learning where an AI model is trained on labeled data. The model learns to map inputs to known outputs (labels) to make predictions on new, unseen data.
It involves feeding the algorithm with input-output pairs, enabling it to learn patterns and relationships between them.
In supervised learning, the dataset includes both features (input variables) and corresponding labels (output variables). The model uses this labeled data during training to minimize prediction errors.
Common algorithms include linear regression, logistic regression, support vector machines (SVM), decision trees, and neural networks.
Supervised learning is widely used for classification tasks (e.g., spam detection) and regression tasks (e.g., price prediction).
Supervised learning uses labeled data with known outputs, while unsupervised learning works with unlabeled data to find patterns or groupings without predefined labels.
Common algorithms include linear regression, logistic regression, decision trees, support vector machines (SVM), and neural networks.
Supervised learning is ideal for classification problems (e.g., email spam detection) and regression problems (e.g., price prediction).
Models are evaluated using metrics like accuracy, precision, recall, F1-score for classification, and mean squared error or R-squared for regression.
Yes, many supervised learning algorithms and frameworks are optimized to handle large-scale datasets efficiently.
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