Oracle Database 19c: Data Mining Techniques — Lesson 2: Classification, Regression, Feature Influence, and Predictive Evaluation

Learning Objectives

• Explain the architecture, workflow, or business purpose behind classification algorithms and target probability concepts.
• Apply regression algorithms, numeric prediction, and residual behavior and feature selection, importance, nested data awareness, and transformations in a controlled scenario.
• Validate outcomes and troubleshoot risks involving confusion matrices, lift, ROC, precision, recall, error metrics, and thresholds.

Detailed Topics

• classification algorithms and target probability concepts
• regression algorithms, numeric prediction, and residual behavior
• feature selection, importance, nested data awareness, and transformations
• confusion matrices, lift, ROC, precision, recall, error metrics, and thresholds

Instructor Demonstration

Demonstrate classification algorithms and target probability concepts within a customer-risk dataset modeled and scored inside Oracle Database with transparent validation and deployment controls, then capture the exact configuration, command, transaction, report, model, query, code, or process evidence that proves the result for lesson 2.

Hands-On Exercise

Implement a controlled exercise covering regression algorithms, numeric prediction, and residual behavior and feature selection, importance, nested data awareness, and transformations, then introduce and resolve an exception involving confusion matrices, lift, ROC, precision, recall, error metrics, and thresholds while preserving before-and-after evidence.

1. Confirm the environment, identities, inputs, dependencies, risks, and expected outcome.
2. Complete the course-specific configuration, development, analysis, or process workflow.
3. Capture settings, commands, data, outputs, logs, reports, screenshots, calculations, or test results.
4. Compare the observed result with acceptance criteria and document corrective action.

Scenario Analysis

• Identify the technical or business decision that must be made.
• Compare valid approaches and their security, performance, reliability, cost, compliance, and support trade-offs.
• Define rollback, recovery, ownership, communication, and evidence requirements.

Troubleshooting Guidance

Diagnose issues across classification algorithms and target probability concepts, regression algorithms, numeric prediction, and residual behavior, feature selection, importance, nested data awareness, and transformations, confusion matrices, lift, ROC, precision, recall, error metrics, and thresholds without making an unverified production change. Separate symptoms from causes, gather evidence, test the smallest reversible correction, rerun validation, and record preventive action.

Validation Checklist

• The requirement, scope, inputs, dependencies, and expected result are documented.
• The result is reproducible and supported by course-specific evidence.
• Normal, boundary, failure, security, performance, and operational conditions are reviewed.
• Exceptions, rollback, ownership, and the next improvement priority are recorded.

Lesson Outcome

Learners can apply Classification, Regression, Feature Influence, and Predictive Evaluation in a realistic Oracle Database 19c: Data Mining Techniques scenario and validate the result with documented evidence.