How does supervised learning differ from unsupervised learning in machine learning?
Supervised learning and unsupervised learning are two fundamental approaches in machine learning. Supervised learning is a type of learning where the algorithm is provided with labeled input data, meaning it has both inputs and their corresponding outputs. The goal is to learn a mapping function that can predict the output for new, unseen inputs. Unsupervised learning, on the other hand, deals with unlabeled data where the algorithm only has access to input data without any corresponding output labels. The objective of unsupervised learning is typically to discover patterns, structures, or relationships within the data.
Long answer
Supervised Learning: Supervised learning algorithms rely on labeled training data to learn a mapping function that maps input variables (features) to output variables (labels). This means that during training, the algorithm is provided with both inputs and their corresponding correct outputs. With this information, the algorithm learns patterns and relationships in the data and generalizes its understanding to make predictions about new unseen data points.
Supervised learning consists of two main types: regression and classification. In regression problems, the supervised algorithm predicts continuous numeric values as outputs based on input features. For instance, predicting house prices based on features like size, number of bedrooms, location, etc., would be a regression problem. Conversely, classification problems involve classifying inputs into predefined categories or classes. Given features such as petal length and width of flowers, a classifier can predict which species of flower it belongs to (e.g., Iris setosa or Iris versicolor).
Unsupervised Learning: In contrast to supervised learning, unsupervised learning deals with unlabeled data where there are no predefined output labels for the given inputs. The primary goal of unsupervised learning algorithms is broader - discovering patterns or underlying structures within the data without specific guidance on what these patterns might be.
Unsupervised learning encompasses several techniques such as clustering and dimensionality reduction. Clustering algorithms aim to group similar instances together based on their characteristics, forming clusters or subgroups. An example of clustering would be grouping customers with similar purchasing behaviors for targeted marketing strategies. Dimensionality reduction techniques focus on reducing the number of variables or features in the data while preserving its meaningful information. This is useful for visualizing high-dimensional data or improving computational efficiency.
Unsupervised learning can also be used as a preprocessing step for supervised learning tasks. By discovering hidden patterns in the unlabeled data, unsupervised methods can extract informative features that can then be used as input for a subsequent supervised learning algorithm.
In summary, the key difference between supervised and unsupervised learning lies in whether the training data has labeled outputs or not. Supervised learning aims to predict labels based on inputs using labeled training data, whereas unsupervised learning focuses on discovering patterns or structures within the unlabeled data without any predefined output labels.