Supervised, unsupervised, and semi-supervised machine learning (ML)

The main approaches to machine learning (ML) are supervised, unsupervised, and semi-supervised, each of which suits different scenarios and available data.


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Machine learning (ML) is a branch of artificial intelligence (AI) that enables systems to learn and improve from experience without explicit programming. There are three main approaches: supervised, unsupervised, and semi-supervised machine learning (ML), each distinct in its methodology, application, outcomes, and use cases.

Supervised machine learning

Supervised learning involves training an AI algorithm using unlabeled data. Here, developers provide input data (features) and corresponding output labels to the ML model. The algorithm learns to map inputs to outputs by identifying patterns and relationships within the dataset.

Two common supervised learning techniques include:

  • Regression: Predicts continuous values, such as predicting house prices based on features like area, location, etc.
  • Classification: Categorizes data into discrete classes, such as identifying spam emails versus legitimate ones 

Supervised machine learning is used in various applications, such as:

  • Image recognition: Identifying objects or characters in images
  • Sentiment analysis: Determining sentiment (positive, negative, or neutral) from text data for social media monitoring or customer feedback analysis
  • Medical diagnosis: Identifying diseases based on patient symptoms and medical history
  • Stock market prediction: Forecasting stock prices based on historical data and market indicators
  • Spam email categorization: Classifying emails as spam or legitimate based on content and features
  • Real estate market prediction: Predicting housing prices based on features like area, location, and amenities

Unsupervised machine learning

Unsupervised learning involves training algorithms on unlabeled data and attempts to find hidden patterns or intrinsic structures within the dataset. The model explores the data without explicit oversight or guidance, making autonomous inferences or organizing data based on similarities or differences.

Two popular unsupervised learning techniques are:

  • Clustering: Grouping similar data points in such a way that points in the same cluster are more similar to each other than to those in other groups
  • Dimensionality reduction: Reducing the number of random variables under consideration by obtaining a set of principal variables, thereby simplifying the data while retaining important information
  • Association mining: Discovering interesting relationships, associations, or correlations among variables or items in large datasets

Unsupervised machine learning is useful in applications such as:

  • Market basket analysis: Understanding purchasing patterns in retail, such as which items are frequently bought together
  • Anomaly detection: Identifying unusual behavior in network traffic for cybersecurity purposes
  • Customer segmentation: Grouping customers based on purchasing behavior for personalized marketing campaigns
  • Pattern recognition: Detecting patterns in large retail datasets to detect market trends
  • Recommendation systems: Identifying similarities between users or items to suggest products or content that might interest a user
  • Image and document clustering: Organizing large collections of images or documents for easier retrieval, categorization, or understanding of underlying themes

Semi-supervised machine learning

Finally, semi-supervised learning combines elements of both supervised and unsupervised learning. It uses a limited amount of labeled data, along with a more extensive pool of labeled data, to improve the model's accuracy and performance.

Common semi-supervised learning approaches include:

  • Self-training: Initially training the algorithm on labeled data, and then using the model to assign labels to unlabeled data with high-confidence predictions for further training
  • Co-training: Utilizing multiple views of the data (that is, different feature representations) to allow the model to learn from both labeled and unlabeled data samples

Some use cases for semi-supervised machine learning are:

  • Speech recognition: Using a small set of labeled audio data combined with a larger collection of unlabeled data to improve the accuracy at which the model recognizes and comprehends speech
  • Document classification: Utilizing labeled documents along with a larger set of unlabeled documents to classify new ones efficiently
  • Language translation: Enhancing translation accuracy by training on limited parallel corpora along with vast amounts of monolingual data
  • Fraud detection: Identifying fraudulent transactions by training on labeled fraud data and a larger pool of normal transactions

Supervised, unsupervised, and semi-supervised learning: differences and applications

Supervised learning: With its reliance on labeled data, excels in scenarios where clear, labeled samples are available. It's proficient in tasks such as classification and regression. 

Unsupervised learning: Deals with unlabeled data, making it valuable in exploratory data analysis, finding hidden structures, and reducing dimensionality.

Semi-supervised learning: Bridges the gap by combining labeled and unlabeled data. This approach is beneficial when gathering labeled data is expensive or time-consuming. This approach allows models to harness larger amounts of readily available but unlabeled data for enhanced model performance.

The choice between supervised, unsupervised, or semi-supervised machine learning depends on the nature of available data, the task at hand, and the desired outcome. Each methodology offers distinctive strengths, catering to diverse machine-learning needs across various domains and industries.