Knowledge Base

What is Supervised Learning?

09/20/2023 | By: FDS

Supervised learning is a machine learning approach in which an algorithm learns from labeled training data to make predictions or decisions. It involves providing the algorithm with input-output pairs, where the input (also called features or attributes) represents the data, and the output (also called labels or targets) represents the corresponding desired prediction or classification.

The goal of supervised learning is for the algorithm to learn a mapping or function that can generalize from the provided labeled examples to make accurate predictions or decisions on unseen or future data. The algorithm learns by identifying patterns, relationships, or statistical properties in the training data, and then uses this knowledge to make predictions or classifications on new, unlabeled data.

Supervised learning can be further categorized into two main types:

Classification: In classification tasks, the algorithm learns to assign predefined labels or classes to input data based on the patterns observed in the training examples. For example, given a dataset of emails labeled as "spam" or "not spam," a classification algorithm can learn to classify new, unseen emails as either spam or not spam.

Regression: In regression tasks, the algorithm learns to predict a continuous numerical value or a numeric quantity based on the input data. For instance, given a dataset of housing prices with corresponding features such as size, location, and number of rooms, a regression algorithm can learn to predict the price of a new, unseen house.

In both classification and regression, the performance of the supervised learning algorithm is typically evaluated using evaluation metrics such as accuracy, precision, recall, or mean squared error, depending on the specific problem domain.

Supervised learning is widely used in various applications, including image recognition, natural language processing, sentiment analysis, fraud detection, and many others, where labeled data is available to train the algorithm.

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