Oracle Machine Learning for Python on Oracle Cloud — Lesson 3: OML Algorithms, AutoML, Evaluation, and Explainability

Learning Objectives

• Explain the purpose and operating context of classification, regression, clustering, and anomaly algorithms.
• Apply model settings, training data, partitions, and algorithm assumptions and AutoML algorithm selection, feature selection, tuning, and ranking in a controlled environment.
• Validate outcomes and troubleshoot risks involving metrics, ROC, confusion matrix, importance, explainability, and acceptance.

Detailed Topics

• classification, regression, clustering, and anomaly algorithms
• model settings, training data, partitions, and algorithm assumptions
• AutoML algorithm selection, feature selection, tuning, and ranking
• metrics, ROC, confusion matrix, importance, explainability, and acceptance

Instructor Demonstration

Demonstrate classification, regression, clustering, and anomaly algorithms for a Python data-science workflow moved from local memory to in-database execution and 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 Oracle Machine Learning for Python on Oracle Cloud exercise covering model settings, training data, partitions, and algorithm assumptions and AutoML algorithm selection, feature selection, tuning, and ranking; introduce an exception involving metrics, ROC, confusion matrix, importance, explainability, and 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, regression, clustering, and anomaly algorithms, model settings, training data, partitions, and algorithm assumptions, AutoML algorithm selection, feature selection, tuning, and ranking, metrics, ROC, confusion matrix, importance, explainability, and 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 OML Algorithms, AutoML, Evaluation, and Explainability in a realistic Oracle Machine Learning for Python on Oracle Cloud scenario and defend the result with documented evidence.