1. Machine Learning for SQL Use Cases
  2. Concepts
  3. Machine Learning Algorithms
  4. About Explicit Semantic Analysis
  5. Data Preparation for ESA

Data Preparation for ESA

Automatic Data Preparation normalizes input vectors to a unit length for Explicit Semantic Analysis (ESA).

When there are missing values in columns with simple data types (not nested), ESA replaces missing categorical values with the mode and missing numerical values with the mean. When there are missing values in nested columns, ESA interprets them as sparse. The algorithm replaces sparse numeric data with zeros and sparse categorical data with zero vectors. The Oracle Machine Learning for SQL data preparation transforms the input text into a vector of real numbers. These numbers represent the importance of the respective words in the text.

See Also:

DBMS_DATA_MINING —Algorithm Settings: Explicit Semantic Analysis for a listing and explanation of the available model settings.

Note:

The term hyperparameter is also interchangeably used for model setting.