Machine Learning on Oracle — Lesson 2: Classification and Regression Models, Metrics, and Business Thresholds

Learning Objectives

• Explain the purpose and operating context of classification algorithms, probabilities, classes, and cost-sensitive decisions.
• Apply regression models, numeric targets, residuals, and error behavior and confusion matrix, ROC, lift, precision, recall, RMSE, and MAE in a controlled environment.
• Validate outcomes and troubleshoot risks involving threshold selection, holdout validation, calibration, and business acceptance.

Detailed Topics

• classification algorithms, probabilities, classes, and cost-sensitive decisions
• regression models, numeric targets, residuals, and error behavior
• confusion matrix, ROC, lift, precision, recall, RMSE, and MAE
• threshold selection, holdout validation, calibration, and business acceptance

Instructor Demonstration

Demonstrate classification algorithms, probabilities, classes, and cost-sensitive decisions for a customer-risk use case modeled inside Oracle Database from source data to governed scoring; capture the exact configuration, code, command, transaction, query, dashboard, model, schedule, or report evidence that proves the expected result.

Hands-On Exercise

Complete a controlled Machine Learning on Oracle exercise covering regression models, numeric targets, residuals, and error behavior and confusion matrix, ROC, lift, precision, recall, RMSE, and MAE; introduce an exception involving threshold selection, holdout validation, calibration, and business acceptance, diagnose it from evidence, apply the smallest reversible correction, and rerun acceptance checks.

1. Confirm scope, prerequisites, identities, dependencies, data, risks, and expected outcome.
2. Perform the course-specific configuration, development, administration, analysis, or business-process workflow.
3. Capture commands, settings, records, logs, screenshots, reports, metrics, or test results.
4. Compare the observed result with acceptance criteria and document corrective or preventive action.

Scenario Analysis

• Identify the technical or business decision and the stakeholders affected by it.
• Compare security, performance, reliability, compliance, cost, usability, and support trade-offs.
• Define rollback, recovery, communication, evidence, ownership, and escalation requirements.

Troubleshooting Guidance

Diagnose issues involving classification algorithms, probabilities, classes, and cost-sensitive decisions, regression models, numeric targets, residuals, and error behavior, confusion matrix, ROC, lift, precision, recall, RMSE, and MAE, threshold selection, holdout validation, calibration, and business acceptance without making an unverified production change. Separate symptoms from causes, gather time-correlated evidence, test a reversible hypothesis, validate the correction, and record recurrence controls.

Validation Checklist

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

Lesson Outcome

Learners can apply Classification and Regression Models, Metrics, and Business Thresholds in a realistic Machine Learning on Oracle scenario and defend the result with documented evidence.