Supervised learning and unsupervised learning are two major categories of machine learning techniques that differ in the way they learn from data.
Supervised learning involves training a machine learning model on a labeled dataset, where each data point is associated with a target variable or output. The goal of the algorithm is to learn a mapping function between the input features and the target variable, such that it can accurately predict the target variable for new, unseen data. For example, a supervised learning algorithm might be trained to predict the price of a house based on its features, such as the number of bedrooms, square footage, and location.
In contrast, unsupervised learning involves training a machine learning model on an unlabeled dataset, where there is no target variable or output. The goal of the algorithm is to discover patterns and relationships within the data without any guidance or supervision from a human expert. For example, an unsupervised learning algorithm might be used to group similar customers together based on their purchase behavior, without any prior knowledge of which customers belong to which segments.
The key differences between supervised and unsupervised learning can be summarized as follows:
Labeled vs. Unlabeled Data: Supervised learning uses labeled data, where each data point is associated with a target variable or output, while unsupervised learning uses unlabeled data, where there is no target variable or output.
Goal: The goal of supervised learning is to learn a mapping function between the input features and the target variable, such that it can accurately predict the target variable for new, unseen data. The goal of unsupervised learning is to discover patterns and relationships within the data without any guidance or supervision from a human expert.
Applications: Supervised learning is commonly used for classification and regression problems, such as image classification, sentiment analysis, and stock price prediction. Unsupervised learning is commonly used for clustering, anomaly detection, and dimensionality reduction, such as customer segmentation, fraud detection, and image compression.
In summary, while supervised learning and unsupervised learning are both important machine learning techniques, they differ in the type of data they use, their goals, and the applications they are commonly used for.
