Part II Machine Learning Techniques

Part II provides basic conceptual information about machine learning techniques that the Oracle Machine Learning supports.

Machine learning techniques represent a class of problems that can be solved using Oracle Machine Learning algorithms.

Note:

The term machine learning technique has no relationship to a SQL language function.

Part II contains these chapters:

• Anomaly Detection
  Learn how to detect rare cases in the data through anomaly detection, an unsupervised function.
• Association
  Discover association rules using the unsupervised machine learning technique of association to find co-occurrences in data.
• Classification
  Predict categorical targets using classification, a supervised machine learning technique.
• Clustering
  Discover natural groupings in data using clustering, an unsupervised machine learning technique.
• Embedding
  Explore embedding as a machine learning technique that transforms data in numeric dimensions that are represented as vectors to enable content similarity search and other applications.
• Feature Extraction
  Learn how to perform attribute reduction using feature extraction as an unsupervised function.
• Feature Selection
  Learn how to perform feature selection and attribute importance.
• Ranking
  Use ranking as a regression machine learning technique to prioritize items.
• Regression
  Learn how to predict a continuous numerical target through regression - the supervised machine learning technique.
• Row Importance
  Use row importance as an unsupervised technique to preprocess data before model building with other machine learning techniques.
• Time Series
  Learn about time series as an Oracle Machine Learning regression function.