Classification (Supervised Learning)
The goal of classification is to assign predefined labels to input data based on learned patterns. This task is commonly used in applications such as spam detection, medical diagnosis, and image recognition. Classification models are trained on labeled data, allowing them to categorize new, unseen data into specific classes. Algorithms like decision trees, support vector machines (SVM), and neural networks are frequently used for classification tasks. Classification falls under supervised learning, as it relies on labeled datasets for training.
Regression (Supervised Learning)
Regression aims to predict continuous values based on input data. Unlike classification, where the output is categorical, regression models provide numerical predictions. This is particularly useful in applications such as price forecasting, weather prediction, and stock market analysis. Linear regression, polynomial regression, and neural networks are popular methods used in regression analysis. Like classification, regression is a form of supervised learning, as it also requires labeled data.
Clustering (Unsupervised Learning)
Clustering is an unsupervised learning technique that groups data points with similar characteristics into clusters. The primary objective is to identify natural structures in data without prior knowledge of the categories. This approach is widely used in customer segmentation, image segmentation, and anomaly detection. Common clustering algorithms include k-means, hierarchical clustering, and DBSCAN. Since clustering does not rely on labeled data, it is classified as unsupervised learning.
Association (Unsupervised Learning)
Association rule learning focuses on discovering relationships and patterns between variables in large datasets. It is widely used in market basket analysis, where retailers seek to identify products that are frequently purchased together. The goal is to extract meaningful insights that can be used to optimize sales strategies and customer recommendations. Popular techniques include the Apriori algorithm and FP-Growth algorithm. Association rule learning is considered unsupervised learning, as it finds patterns without labeled training data.
Anomaly Detection (Supervised, Unsupervised, Semi-Supervised Learning)
Anomaly detection, or outlier detection, aims to identify rare or unusual data points that deviate significantly from the norm. This technique is essential for fraud detection, network security, and medical diagnostics. The objective is to detect patterns that indicate potential threats or abnormalities in a system. Techniques such as statistical methods, isolation forests, and autoencoders are often employed for anomaly detection. Depending on the approach, anomaly detection can be supervised, unsupervised or semi-supervised.