Oracle Machine Learning Basics

2.1 Machine Learning Functions

Each machine learning function specifies a class of problems that can be modeled and solved.

A basic understanding of machine learning functions and algorithms is required for using Oracle Machine Learning.

Machine learning functions fall generally into two categories: supervised and unsupervised. Notions of supervised and unsupervised learning are derived from the science of machine learning, which has been called a sub-area of artificial intelligence.

Artificial intelligence refers to the implementation and study of systems that exhibit autonomous intelligence or behavior of their own. Machine learning deals with techniques that enable devices to learn from their own performance and modify their own functioning.

The following illustration provides an idea of how to use Oracle machine learning functions.

Figure 2-1 How to Use Machine Learning Functions

Description of "Figure 2-1 How to Use Machine Learning Functions"

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Algorithms

2.1.1 Supervised Machine Learning

Overview of supervised machine learning.

Supervised learning is also known as directed learning. The learning process is directed by a previously known dependent attribute or target. Directed Oracle Machine Learning attempts to explain the behavior of the target as a function of a set of independent attributes or predictors.

Supervised learning generally results in predictive models. This is in contrast to unsupervised learning where the goal is pattern detection.

2.1.1.1 Supervised Learning: Testing

The process of applying the model to test data helps to determine whether the model, built on one chosen sample, is generalizable to other data. In other words, test data is used for scoring.

In particular, it helps to avoid the phenomenon of overfitting, which can occur when the logic of the model fits the build data too well and therefore has little predictive power.

2.1.1.2 Supervised Learning: Scoring

Learn about scoring in supervised learning.

Apply data, also called scoring data, is the actual population to which a model is applied. For example, you might build a model that identifies the characteristics of customers who frequently buy a certain product. To obtain a list of customers who shop at a certain store and are likely to buy a related product, you might apply the model to the customer data for that store. In this case, the store customer data is the scoring data.

Most supervised learning can be applied to a population of interest. The principal supervised machine learning techniques, classification and regression, can both be used for scoring.

Oracle Machine Learning does not support the scoring operation for attribute importance, another supervised function. Models of this type are built on a population of interest to obtain information about that population; they cannot be applied to separate data. An attribute importance model returns and ranks the attributes that are most important in predicting a target value.

Oracle Machine Learning supports the supervised machine learning functions described in the following table:

Table 2-1 Oracle Machine Learning Supervised Functions

Function	Description	Sample Problem
Attribute Importance	Identifies the attributes that are most important in predicting a target attribute	Given customer response to an affinity card program, find the most significant predictors
Classification	Assigns items to discrete classes and predicts the class to which an item belongs	Given demographic data about a set of customers, predict customer response to an affinity card program
Regression	Approximates and forecasts continuous values	Given demographic and purchasing data about a set of customers, predict customers' age

2.1.2 Unsupervised Machine Learning

Overview of unsupervised machine learning.

Unsupervised learning is non-directed. There is no distinction between dependent and independent attributes. There is no previously-known result to guide the algorithm in building the model.

Unsupervised learning can be used for descriptive purposes. It can also be used to make predictions.

2.1.2.1 Unsupervised Learning: Scoring

Introduces unsupervised learning, supported scoring operations, and unsupervised machine learning functions.

Although unsupervised machine learning does not specify a target, most unsupervised learning can be applied to a population of interest. For example, clustering models use descriptive machine learning techniques, but they can be applied to classify cases according to their cluster assignments. Anomaly Detection, although unsupervised, is typically used to predict whether a data point is typical among a set of cases.

Oracle Machine Learning supports the scoring operation for Clustering and Feature Extraction, both unsupervised machine learning functions. Oracle Machine Learning does not support the scoring operation for Association Rules, another unsupervised function. Association models are built on a population of interest to obtain information about that population; they cannot be applied to separate data. An association model returns rules that explain how items or events are associated with each other. The association rules are returned with statistics that can be used to rank them according to their probability.

OML supports the unsupervised functions described in the following table:

Table 2-2 Oracle Machine Learning Unsupervised Functions

Function	Description	Sample Problem
Anomaly Detection	Identifies items (outliers) that do not satisfy the characteristics of "normal" data	Given demographic data about a set of customers, identify customer purchasing behavior that is significantly different from the norm
Association Rules	Finds items that tend to co-occur in the data and specifies the rules that govern their co-occurrence	Find the items that tend to be purchased together and specify their relationship
Clustering	Finds natural groupings in the data	Segment demographic data into clusters and rank the probability that an individual belongs to a given cluster
Feature Extraction	Creates new attributes (features) using linear combinations of the original attributes	Given demographic data about a set of customers, group the attributes into general characteristics of the customers

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2.2 Algorithms

An algorithms is a mathematical procedure for solving a specific kind of problem. For some machine learning functions, you can choose among several algorithms.

Each algorithm produces a specific type of model, with different characteristics. Some machine learning problems can best be solved by using more than one algorithm in combination. For example, you might first use a feature extraction model to create an optimized set of predictors, then a classification model to make a prediction on the results.

2.2.1 Oracle Machine Learning Supervised Algorithms

Oracle Machine Learning for SQL (OML4SQL) supports the supervised machine learning algorithms described in the following table.

Table 2-3 Oracle Machine Learning Algorithms for Supervised Functions

Algorithm	Function	Description
Decision Tree	Classification	Decision trees extract predictive information in the form of human-understandable rules. The rules are if-then-else expressions; they explain the decisions that lead to the prediction.
Explicit Semantic Analysis	Classification	Explicit Semantic Analysis (ESA) is designed to make predictions for text data. This algorithm can address use cases with hundreds of thousands of classes. In Oracle Database 12c Release 2, ESA was introduced as Feature Extraction algorithm.
Exponential Smoothing	Time Series	Exponential Smoothing (ESM) provides forecasts for time series data. Forecasts are made for each time period within a user-specified forecast window. ESM provides a total of 14 different time series models, including all the most popular estimates of trend and seasonal effects. Choice of model is controlled by user settings. ESM provides confidence bounds on its forecasts.
Generalized Linear Model	Classification and Regression	Generalized Linear Model (GLM) implements logistic regression for classification of binary targets and linear regression for continuous targets. GLM classification supports confidence bounds for prediction probabilities. GLM regression supports confidence bounds for predictions.
Minimum Description Length	Attribute Importance	Minimum Description Length (MDL) is an information theoretic model selection principle. MDL assumes that the simplest, most compact representation of data is the best and most probable explanation of the data.
Naive Bayes	Classification	Naive Bayes makes predictions using Bayes' Theorem, which derives the probability of a prediction from the underlying evidence, as observed in the data.
Neural Network	Classification and Regression	Neural Network in machine learning is an artificial algorithm inspired from biological neural network and is used to estimate or approximate functions that depend on a large number of generally unknown inputs. Neural Network is designed for classification and regression.
Random Forest	Classification	Random Forest is a powerful machine learning algorithm. The Random Forest algorithm builds a number of Decision Tree models and predicts using the ensemble of trees.
Support Vector Machine	Classification and Regression	Distinct versions of the Support Vector Machine (SVM) algorithm use different kernel functions to handle different types of data sets. Linear and Gaussian (nonlinear) kernels are supported. SVM classification attempts to separate the target classes with the widest possible margin. SVM regression tries to find a continuous function such that the maximum number of data points lie within an epsilon-wide tube around it.
XGBoost	Classification and Regression	XGBoost is machine learning algorithm for regression and classification that makes available the XGBoost open source package. Oracle Machine Learning for SQL XGBoost prepares training data, invokes XGBoost, builds and persists a model, and applies the model for prediction.

2.2.2 Oracle Machine Learning Unsupervised Algorithms

Oracle Machine Learning for SQL (OML4SQL) supports the unsupervised machine learning algorithms described in the following table.

Table 2-4 Oracle Machine Learning Algorithms for Unsupervised Functions

Algorithm	Function	Description
Apriori	Association	Apriori performs market basket analysis by identifying co-occurring items (frequent itemsets) within a set. Apriori finds rules with support greater than a specified minimum support and confidence greater than a specified minimum confidence.
CUR Matrix Decomposition	Attribute Importance	CUR Matrix Decomposition is an alternative to Support Vector Machine (SVM) and Principal Component Analysis (PCA) and an important tool for exploratory data analysis. This algorithm performs analytical processing and singles out important columns and rows.
Expectation Maximization	Clustering	Expectation Maximization (EM) is a density estimation algorithm that performs probabilistic clustering. In density estimation, the goal is to construct a density function that captures how a given population is distributed. The density estimate is based on observed data that represents a sample of the population. Oracle Machine Learning supports probabilistic clustering and data frequency estimates and other applications of Expectation Maximization.
Explicit Semantic Analysis	Feature Extraction	Explicit Semantic Analysis (ESA) uses existing knowledge base as features. An attribute vector represents each feature or a concept. ESA creates a reverse index that maps every attribute to the knowledge base concepts or the concept-attribute association vector value.
k-Means	Clustering	k-Means is a distance-based clustering algorithm that partitions the data into a predetermined number of clusters. Each cluster has a centroid (center of gravity). Cases (individuals within the population) that are in a cluster are close to the centroid. OML4SQL supports an enhanced version of k-Means. It goes beyond the classical implementation by defining a hierarchical parent-child relationship of clusters.
Multivariate State Estimation Technique - Sequential Probability Ratio Test	Anomaly Detection	The Multivariate State Estimation Technique - Sequential Probability Ratio Test (MSET-SPRT) algorithm is a nonlinear, nonparametric anomaly detection machine learning technique designed for monitoring critical processes. It detects subtle anomalies while also producing minimal false alarms.
Non-Negative Matrix Factorization	Feature Extraction	Non-Negative Matrix Factorization (NMF) generates new attributes using linear combinations of the original attributes. The coefficients of the linear combinations are non-negative. During model apply, an NMF model maps the original data into the new set of attributes (features) discovered by the model.
One Class Support Vector Machine	Anomaly Detection	One-class SVM builds a profile of one class. When the model is applied, it identifies cases that are somehow different from that profile. This allows for the detection of rare cases that are not necessarily related to each other.
Orthogonal Partitioning Clustering	Clustering	Orthogonal Partitioning Clustering (O-Cluster) creates a hierarchical, grid-based clustering model. The algorithm creates clusters that define dense areas in the attribute space. A sensitivity parameter defines the baseline density level.
Singular Value Decomposition and Principal Component Analysis	Feature Extraction	Singular Value Decomposition (SVD) and Principal Component Analysis (PCA) are orthogonal linear transformations that are optimal at capturing the underlying variance of the data. This property is extremely useful for reducing the dimensionality of high-dimensional data and for supporting meaningful data visualization. In addition to dimensionality reduction, SVD and PCA have a number of other important applications, such as data de-noising (smoothing), data compression, matrix inversion, and solving a system of linear equations.

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Algorithms

2.3 Data Preparation

Preparing the data is a valuable step in solving machine learning problems.

The quality of a model depends to a large extent on the quality of the data used to build (train) it. Much of the time spent in any given machine learning project is devoted to data preparation. The data must be carefully inspected, cleansed, and transformed, and algorithm-appropriate data preparation methods must be applied.

The process of data preparation is further complicated by the fact that any data to which a model is applied, whether for testing or for scoring, must undergo the same transformations as the data used to train the model.

2.3.1 Oracle Machine Learning for SQL Simplifies Data Preparation

Learn about various features of Oracle Machine Learning for SQL for data preparation.

OML4SQL offers several features that significantly simplify the process of data preparation:

Embedded data preparation: The transformations used in training the model are embedded in the model and automatically run whenever the model is applied to new data. If you specify transformations for the model, you only have to specify them once.
Automatic Data Preparation (ADP): Oracle Machine Learning for SQL supports an automated data preparation mode. When ADP is active, Oracle Machine Learning for SQL automatically performs the data transformations required by the algorithm. The transformation instructions are embedded in the model along with any user-specified transformation instructions.
Automatic management of missing values and sparse data: Oracle Machine Learning for SQL uses consistent methodology across machine learning algorithms to handle sparsity and missing values.
Transparency: Oracle Machine Learning for SQL provides model details, which are a view of the attributes that are internal to the model. This insight into the inner details of the model is possible because of reverse transformations, which map the transformed attribute values to a form that can be interpreted by a user. Where possible, attribute values are reversed to the original column values. Reverse transformations are also applied to the target of a supervised model, thus the results of scoring are in the same units as the units of the original target.
Tools for custom data preparation: Oracle Machine Learning for SQL provides many common transformation routines in the DBMS_DATA_MINING_TRANSFORM PL/SQL package. You can use these routines, or develop your own routines in SQL, or both. The SQL language is well suited for implementing transformations in the database. You can use custom transformation instructions along with ADP or instead of ADP.

2.3.2 Case Data

Learn the importance of case data in machine learning.

Most machine learning algorithms act on single-record case data, where the information for each case is stored in a separate row. The data attributes for the cases are stored in the columns.

When the data is organized in transactions, the data for one case (one transaction) is stored in many rows. An example of transactional data is market basket data. With the single exception of Association Rules, which can operate on native transactional data, Oracle Machine Learning for SQL algorithms require single-record case organization.

2.3.2.1 Nested Data

Learn how nested columns are treated in Oracle Machine Learning for SQL.

OML4SQL supports attributes in nested columns. A transactional table can be cast as a nested column and included in a table of single-record case data. Similarly, star schemas can be cast as nested columns. With nested data transformations, Oracle Machine Learning for SQL can effectively mine data originating from multiple sources and configurations.

2.3.3 Text Data

Prepare and transform unstructured text data for machine learning.

Oracle Machine Learning for SQL interprets CLOB columns and long VARCHAR2 columns automatically as unstructured text. Additionally, you can specify columns of short VARCHAR2, CHAR, BLOB, and BFILE as unstructured text. Unstructured text includes data items such as web pages, document libraries, Power Point presentations, product specifications, emails, comment fields in reports, and call center notes.

OML4SQL uses Oracle Text utilities and term weighting strategies to transform unstructured text for analysis. In text transformation, text terms are extracted and given numeric values in a text index. The text transformation process is configurable for the model and for individual attributes. Once transformed, the text can by mined with a OML4SQL algorithm.

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2.4 In-Database Scoring

Scoring is the application of a machine learning algorithm to new data. In Oracle Machine Learning for SQL scoring engine and the data both reside within the database.

In traditional machine learning, models are built using specialized software on a remote system and deployed to another system for scoring. This is a cumbersome, error-prone process open to security violations and difficulties in data synchronization.

With OML4SQL, scoring is simple and secure. The scoring engine and the data both reside within the database. Scoring is an extension to the SQL language, so the results of machine learning can easily be incorporated into applications and reporting systems.

2.4.1 Parallel Execution and Ease of Administration

All Oracle Machine Learning for SQL scoring routines support parallel execution for scoring large data sets.

In-database scoring provides performance advantages. All Oracle Machine Learning for SQL scoring routines support parallel execution, which significantly reduces the time required for executing complex queries and scoring large data sets.

In-database machine learning minimizes the IT effort needed to support OML4SQL initiatives. Using standard database techniques, models can easily be refreshed (re-created) on more recent data and redeployed. The deployment is immediate since the scoring query remains the same; only the underlying model is replaced in the database.

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Oracle Database VLDB and Partitioning Guide

2.4.2 SQL Functions for Model Apply and Dynamic Scoring

In Oracle Machine Learning for SQL, scoring is performed by SQL language functions. Understand the different ways of scoring using SQL functions.

The functions perform prediction, clustering, and feature extraction. The functions can be invoked in two different ways: By applying a machine learning model object (Example 2-1), or by executing an analytic clause that computes the machine learning analysis dynamically and applies it to the data (Example 2-2). Dynamic scoring, which eliminates the need for a model, can supplement, or even replace, the more traditional methodology described in "The Machine Learning Process".

In Example 2-1, the PREDICTION_PROBABILITY function applies the model svmc_sh_clas_sample, created in Example 1-1, to score the data in mining_data_apply_v. The function returns the ten customers in Italy who are most likely to use an affinity card.

In Example 2-2, the functions PREDICTION and PREDICTION_PROBABILITY use the analytic syntax (the OVER () clause) to dynamically score the data in mining_data_apply_v. The query returns the customers who currently do not have an affinity card with the probability that they are likely to use.

Example 2-1 Applying a Oracle Machine Learning for SQL Model to Score Data

SELECT cust_id FROM
  (SELECT cust_id, 
        rank() over (order by PREDICTION_PROBABILITY(svmc_sh_clas_sample, 1
                     USING *) DESC, cust_id) rnk
   FROM mining_data_apply_v
   WHERE country_name = 'Italy')
WHERE rnk <= 10
ORDER BY rnk;

   CUST_ID
----------
    101445
    100179
    100662
    100733
    100554
    100081
    100344
    100324
    100185
    101345

Example 2-2 Executing an Analytic Function to Score Data

SELECT cust_id, pred_prob FROM
  (SELECT cust_id, affinity_card, 
    PREDICTION(FOR TO_CHAR(affinity_card) USING *) OVER () pred_card,
    PREDICTION_PROBABILITY(FOR TO_CHAR(affinity_card),1 USING *) OVER () pred_prob
   FROM mining_data_build_v)
WHERE affinity_card = 0
AND pred_card = 1
ORDER BY pred_prob DESC;

   CUST_ID PRED_PROB
---------- ---------
    102434       .96
    102365       .96
    102330       .96
    101733       .95
    102615       .94
    102686       .94
    102749       .93
    .
    .
    .
    101656       .51

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