Demystifying Machine Learning Algorithms in Oracle Analytics: a Guide to Choosing the Right Approach

April 8, 2024 | 4 minute read
Ravi Bhuma
Principal Solutions Architect, Oracle Analytics
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Oracle Analytics Cloud (OAC) and Oracle Analytics Server (OAS) offer a rich repertoire of machine learning algorithms that can be harnessed to reveal patterns, make predictions, and gain insights from your data. In the vast world of data analytics, machine learning stands out as a beacon of hope for making sense of the immense volumes of data that are collected every day.

This blog delves into the intricacies of these algorithms and describes when and how to use them effectively.



Image: Flowchart of Machine Learning Algorithms in Oracle Analytics

The flowchart provides a visual guide through the machine learning algorithm selection process in Oracle Analytics. It delineates the two main learning types, supervised and unsupervised.

  • Supervised Learning: Under supervised learning, it breaks down the path from data type to algorithm choice, distinguishing between classification (binary and multi) and regression tasks, and mapping the relevant algorithms such as SVM, CART, Neural Networks, and Random Forest for classification, as well as Linear Regression and Elastic Net for regression.
  • Unsupervised Learning: The unsupervised learning path illustrates the clustering approach, with algorithms such as K-Means and Hierarchical Clustering.

You can use this diagram as a quick-reference tool for understanding which machine learning algorithms are suitable for various analytical objectives within Oracle Analytics.

Binary Classification: The Art of Decision-Making

When to Use: Binary classification is your go-to choice when you need to sort data into one of two buckets. Think of it as the digital equivalent of the classic "Yes or No" conundrum.

Algorithms at Your Disposal

  • Logistic Regression: Imagine predicting the odds of an event where the outcomes are coded as 1 or 0. Logistic Regression shines here, giving you the probability of occurrence.
  • Support Vector Machine (SVM): SVM is the master of finding the perfect boundary (hyperplane) in a multi-dimensional space that separates your two categories.
  • Classification and Regression Trees (CART): Whether you're dealing with vast datasets or a mix of discrete and continuous variables, CART's decision trees can handle them, offering clarity in a forest of data.
  • Naive Bayes: A probabilistic champion that assumes each feature contributes independently to the outcome, perfect for when you're dealing with a plethora of inputs.
  • Neural Network: The brainchild of machine learning, Neural Networks iteratively learn from their mistakes, adjusting their "neurons" to better predict binary outcomes.
  • Random Forest: Imagine having a team of decision trees where each member casts a vote. The majority wins, providing a robust prediction from collective wisdom.

Real-World Applications

  1. Predicting customer churn: Will they stay, or will they go?
  2. Assessing creditworthiness: To lend or not to lend?
  3. Diagnosing specific diseases: Is it present or absent?

Multi-Classification: Categorizing into the Many

When to Use: Step into the world of multi-classification when two options just don't cut it, and you need to categorize your data into several predetermined classes.

Harnessing Algorithms

The algorithms for multi-classification mirror those of binary classification but with the power to discriminate between more than two classes.

Use Cases

  1. Movie Genre Prediction: Comedy, thriller, or drama?
  2. Customer Segmentation: Which group does the new customer belong to?
  3. Email Sorting: Inbox organization at its finest.

Numerical Prediction: The Quest for Quantitative Forecasts

When to Use: When numbers speak louder than categories, and you need to forecast a numeric value, numerical prediction algorithms take the stage.

Predictive Algorithms

  • Elastic Net Linear Regression: This advanced model doesn't just predict, it selects the most influential variables and combines them in a way that stands the test of multi-collinearity and overfitting.
  • Random Forest & Linear Regression: Both offer a straightforward approach to predicting numerical outcomes, with Random Forest bringing the wisdom of the crowd through its ensemble of trees.
  • CART: Just as it aids in classification, CART's decision trees can predict numerical values, handling both types of data with ease.

Predictive Possibilities

  1. Sales Forecasting: Beyond crystal balls - predicting future sales with precision.
  2. Macroeconomic Modeling: What will the unemployment rates look like? Let the data speak.

Clustering: The Unsupervised Path to Discovery

When to Use: Clustering is your unsupervised learning strategy when you're looking to find natural groupings in your data without predefined labels.

Clustering Algorithms

  • K-Means Clustering: It's like organizing a diverse group of people into circles of common interests based on the nearest "mean" friend.
  • Hierarchical Clustering: Builds a family tree of data points, allowing you to view relationships at different levels of granularity.

Clustering in Action

  1. Market Segmentation: Tailoring marketing strategies to diverse customer groups.
  2. Data Exploration: Unearthing the hidden patterns in your data.

Apply Machine Learning Algorithms in Oracle Analytics

Oracle Analytics enables you to build machine learning into your applications without data scientist expertise. You use the data flow editor to apply the machine learning models to your data.



The suite of machine learning algorithms in Oracle Analytics offer a robust toolkit for extracting meaningful insights from data. Whether through supervised learning with clear-cut outcomes or unsupervised learning that unveils hidden structures, these tools empower data analysts and scientists to make informed decisions, predict future trends, and truly understand the story their data is telling. To find out more, read the documentation or ask questions in the Oracle Analytics Community.

Ravi Bhuma

Principal Solutions Architect, Oracle Analytics

Oracle Analytics Service Excellence, CEAL Team

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